Microarray Gene Expression Datasets Research Articles

Retraction Note: Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO).

Retraction Note: Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO).

Abstract PO5-15-10: Plasticity marker FOXC1 expression accurately predicts efficacy of Adjuvant Tamoxifen + Chemotherapy in reducing all-cause mortality in ER+LN- Breast Cancer: Validation in the SCAN-B Prospective Study (NCT02306096)

Abstract INTRODUCTION: Despite the development and validation of multimarker gene panels like OncotypeDx®, Mammaprint® and EndoPredict®, an affordable and globally accessible biomarker approach that can predict increased risk of all-cause mortality in patients diagnosed with ER+LN- breast cancer, as well as the efficacy of adjuvant endocrine + chemotherapy in mitigating such elevated risk, continues to be an unmet medical need. Lack of such a pragmatic solution continues to be the cause of preventable recurrent/metastatic disease in patients diagnosed with ER+LN- breast cancer in resource-challenged settings around the world. We hypothesized that a predictive biomarker strategy that measures expression of the plasticity marker FOXC1, in combination with clinical parameters like tumor size (TS) and tumor grade (TG), may offer equivalent results to that of the above multimarker gene panels at a significantly lower economic cost without compromising accuracy of prediction results. METHODS: Pre-treatment tumor RNA data obtained from a training cohort (compendium of gene expression microarray datasets, n=2857) and a single large validation cohort (SCAN-B Prospective Multicenter Observational Study, n=3520) for patients diagnosed with ER+LN- breast cancer were analyzed for FOXC1 expression, tumor size (TS) and tumor grade (TG) and correlated with Recurrence-Free Survival (RFS) and Overall Survival (OS). Optimized biomarker cutoff values based on model area-under-curve were leave-one-out cross validated and Risk-of-Recurrence (ROR) prediction algorithm derived utilizing the (compendium) training dataset. The unmodified strategy was then validated in the independent (SCAN-B) validation dataset. RESULTS: A predetermined High ROR score calculated using FOXC1 expression, TS and TG (trained using the compendium dataset) predicted efficacy of adjuvant endocrine + chemotherapy over that of adjuvant endocrine therapy alone in ER+LN- patients in terms of statistically significant reduction in all-cause mortality at 8-years post-diagnosis in the SCAN-B validation dataset (4.66% vs 24.06%, n=326, OR=6.48, 95%CI [2.97-14.11], p< 0.0001). OncotypeDx® (5.7% vs 14.4%, n=762, OR=2.79, 95%CI [1.37-5.67], p=0.002), Mammaprint® (5.1% vs 18.4%, n=665, OR= 95%CI [2.04-8.43], p< 0.0001) and Endopredict® (4.8% vs 15.2%, n=923, OR=3.58 95%CI [1.78-7.21], p=0.0002) were also statistically significant predictors of the same. CONCLUSION: Pre-treatment tumor FOXC1 mRNA or protein expression (assessed using qRT-PCR or routine immunohistochemistry (IHC), respectively, when combined with TS and TG presents a unique and economical alternative solution to multimarker gene panel tests like OncotypeDx®, Mammaprint® or Endopredict®, for guiding therapy of patients diagnosed with ER+LN- breast cancer in resource challenged settings. Such an approach to identify elevated risk of recurrence in patients diagnosed with ER+LN- breast cancer and prevent the same by guiding adjuvant endocrine + chemotherapy decisions, could help to extend recurrence-free and overall survival. Such an approach merits testing in real world ER+LN- patient cohorts in resource-challenged settings to help support implementation of this FOXC1-driven predictive biomarker strategy in the clinic. Citation Format: Partha Ray, Tania Ray, Clive Taylor, Robert Hussa. Plasticity marker FOXC1 expression accurately predicts efficacy of Adjuvant Tamoxifen + Chemotherapy in reducing all-cause mortality in ER+LN- Breast Cancer: Validation in the SCAN-B Prospective Study (NCT02306096) [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 PO5-15-10.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

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.

Abstract 6209: From data disparity to data harmony: A comprehensive pan-cancer omics data collection

Abstract In cancer research, the exponential growth of omics datasets offers a significant opportunity for scientific advancement. However, challenges such as the lack of uniform standards, in both clinical and omic data, hinder the effective utilization of these datasets, thus impeding our understanding of cancer biology and the development of innovative therapeutic approaches.Addressing these challenges, we have created a novel collection of pan-cancer omics datasets with extensive clinical data harmonization and consistent omic data normalization.Here, we focused on patient-derived gene expression microarray datasets from the Gene Expression Omnibus database. To navigate the complexities presented by the diverse clinical descriptions inherent in these datasets, we leveraged our proprietary ontology, machine learning models, and domain expert quality control processes to homogenize the clinical data elements.Datasets were then selected based on sample composition, molecular data compatibility, and clinical data availability, then passed through a uniform preprocessing and normalization pipeline to maximize data quality. Finally, gene names were aligned on a single annotation reference, and potential batch effects were adjusted before expression data were merged together.We obtained a total of 32,825 transcriptomic sample profiles from 470 datasets, covering 13,435 genes and 45 clinical data elements, across 30 cancer types. Healthy tissue was favored over adjacent tissue, to minimize the risk of introducing biases related to cancer patient background genomic profiles into downstream analyses. We compared our collection with The Cancer Genome Atlas (TCGA), the most commonly used RNA-seq transcriptomic dataset in cancer research. It covers 30 out of the 33 TCGA cancer types, with on average 4.2 times more samples per cancer type ([0.3; 45.5], median 3.4). Despite the two data collections being based on distinct technologies, we observed a Pearson correlation of 0.69 over the 11,753 genes in common, and a 100% overlap of the differentially expressed genes between genders. This consistency highlights cross-technology reliability and complementarity.We have built and continuously enriched a comprehensive dataset collection enabling the secondary analysis of high-quality omic data. This initial work - focused on microarray datasets - allows us to streamline design, exploration and validation of various omics data-driven studies in cancer research.Our ongoing efforts involve not only the continued integration of microarray datasets but also the integration of pan-cancer RNA-seq and single-cell data. This initiative is set to expand further, encompassing a broader range of omics datasets in the future. Citation Format: Lea Meunier, Guillaume Appe, Abdelkader Behdenna, Valentin Bernu, Helia Brull Corretger, Prashant Dhillon, Eleonore Fox, Julien Haziza, Charles Lescure, Camille Marijon, Clemence Petit, Solene Weill, Akpeli Nordor. From data disparity to data harmony: A comprehensive pan-cancer omics data collection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6209.

Differentially Expressed Genes in Atopic Dermatitis: Bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus

Introduction:
 Atopic dermatitis (AD) is a chronic and refractory inflammatory skin disease characterized by relapsing eczematous and pruritic skin lesions. Understanding the specific gene expression patterns associated with AD is crucial for advancing diagnosis and targeted treatment development. Using bioinformatics methods, candidate genes and biological pathways involved in AD pathogenesis were identified based on gene expression profiles in the Gene Expression Omnibus (GEO) database.
 Materials and Methods:
 A comprehensive analysis of four pooled transcriptomic datasets obtained from the Gene Expression Omnibus (GEO) database were conducted. Differential gene expression analysis was performed using the GEO2R. The differentially expressed genes (DEGs) between lesion skin of AD patients and normal skin of individuals were analyzed using the Gene Ontology (GO) term enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network to explore the functional roles of these genes.
 Results:
 Among the patient-level gene expression datasets, we identified 133 shared DEGs, consisting of 48 upregulated genes and 85 downregulated genes. GO analyses revealed these DEGs to be significantly enriched in biological processes including inflammatory responses, cytokine-mediated signaling pathway. These DEGs were also enriched in the KEGG pathway, including viral protein interaction with cytokine and cytokine receptor, C-type lectin receptor signaling pathway, cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, and Adipocytokine signaling pathway.
 Conclusion:
 By comparing with other studies using the same method, we found that in addition to the already confirmed pathways such as inflammatory response, different studies have found changes in different hub genes and metabolic pathways, which prompts us to develop individualized treatments for AD.

Abstract B012: Pedigree and multi-omics analyses implicate nucleotide excision repair genes, XPA, ERCC6, and ERCC8, in susceptibility to breast cancer

Abstract Background: The majority of low- and moderate-risk [odds ratios (OR) ≤ 3] susceptibility genes for peri- and post-menopausal breast cancer (≥50 years of age), particularly those with minor allele frequency (MAF) of <30%, have not yet been identified and/or validated. We used a hypothesis-driven integrative approach to identify breast cancer susceptibility genes by analyzing pedigree and multi-omics (genomics, transcriptomics, mutation landscape) data. Methods: We conducted pedigree analysis in 31 families with DNA repair disorders, xeroderma pigmentosum (XP), trichothiodystrophy (TTD), Cockayne syndrome (CS), XP/TTD and XP/CS. These disorders can be caused by mutations in genes involved in the nucleotide excision repair (NER) pathway such as XPA-XPG, ERCC6 and ERCC8. We estimated the frequency of hereditary breast cancer, defined as the presence of at least two cases of premenopausal breast cancer (diagnosed <50 years of age) or one case of premenopausal breast cancer and one case of ovarian cancer in one side of the family, in our study. Furthermore, we conducted individual analysis of four genome-wide association studies (GWAS) datasets (totaling 2961 cases and 4335 controls), individual and meta-analysis of two gene expression microarray datasets (98 cases and 163 controls), and targeted analysis of The Cancer Genome Atlas (TCGA) (522 breast cancers). All analyses involved invasive ductal breast carcinoma in women diagnosed at ≥50 years of age. Results: In an XP family with causative mutations in XPA, there were four cases of premenopausal breast cancer (including heterozygote mutation carriers), one case of ovarian cancer, and one case of prostate cancer. Comprehensive sequencing failed to identify a BRCA1 or BRCA2 mutation in this family. Overall, 7 of 31 (22.6%) families with DNA repair disorders had a family history that was compatible with hereditary breast cancer. Hence, we selected a panel of NER genes as candidates for association studies with breast cancer. Our candidate-gene association analysis of GWAS datasets suggested an increased risk of breast cancer with ERCC6 (main effect:1.29 ≤ OR ≤ 2.91, 0.005 ≤ p ≤ 0.04, 11.8 ≤ MAF ≤ 29.7%) and implicated its interaction with ERCC8 (joint effect:3.03 ≤ OR ≤ 5.31, 0.01 ≤ pinteraction ≤ 0.03). Our transcriptomics analysis identified significant upregulation of ERCC6 (p=7.95 × 10–6) and ERCC8 (p=4.67 × 10–6) in breast cancer. Our analysis of TCGA breast tumor data showed similar mutation frequency of ERCC6 (1.8%) and ERCC8 (0.3%) as known breast cancer susceptibility genes such as BLM (1.9%) and LSP1 (0.3%). Conclusion: Our integrative analyses suggest that ERCC6 may be a previously unreported low- to moderate-risk breast cancer susceptibility gene, which may also interact with ERCC8. ERCC6 and ERCC8 code for the main components of the transcription-coupled (TC) repair sub-pathway of the NER. TC-NER protects against genotoxic agents, such as free radicals and reactive oxygen species. Our results provide a deeper biologic insight and leads for prevention of common breast cancer. Citation Format: Roxana Moslehi, Amiran Dzutsev. Pedigree and multi-omics analyses implicate nucleotide excision repair genes, XPA, ERCC6, and ERCC8, in susceptibility to breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B012.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study

Background: The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease.
 Objective: This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable hub genes in CC.
 Materials and Methods: In this in silico analysis, 2 microarray gene expression datasets; GSE63514 (104 cancer and 24 normal samples), and GSE9750 (42 cancer and 24 normal samples) were extracted from gene expression omnibus to identify common differentially expressed genes between them. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed via the Enrichr database. STRING 12.0 database and CytoHubba plugin in Cytoscape 3.9.1 software were implemented to create and analyze the PPI network. Finally, druggable hub genes were screened.
 Results: Based on the degree method, 10 key genes were known as the hub genes after the screening of PPI networks by the CytoHubba plugin. NCAPG, KIF11, TTK, PBK, MELK, ASPM, TPX2, BUB1, TOP2A, and KIF2C are the key genes, of which 5 genes (KIF11, TTK, PBK, MELK, and TOP2A) were druggable.
 Conclusion: This research provides a novel vision for designing therapeutic targets in patients with CC. However, these findings should be verified through additional experiments.
 Key words: Protein interactions, Cervical cancer, Hub genes, Gene expression, DEGs.

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.

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124–3p, mir-16–5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

Dual regularized subspace learning using adaptive graph learning and rank constraint: Unsupervised feature selection on gene expression microarray datasets

High-dimensional problems have increasingly drawn attention in gene selection and analysis. To add insult to injury, usually the number of features is greater than number of samples in microarray gene dataset which leads to an ill-posed underdetermined equation system. Poor performance and high computational time for learning algorithms are consequences of redundant features in high-dimensional data. Feature selection is a noteworthy pre-processing method to ameliorate the curse of dimensionality with aim of maximum relevancy and minimum redundancy information preservation. Likewise, unsupervised feature selection has been important since collecting labels for data is expensive. In this paper, we develop a novel robust unsupervised feature selection to select discriminative subset of features for unlabeled data based on rank constrained and dual regularized nonnegative matrix factorization. The major focus of the proposed technique is to discard redundant features while keeping the informative features. Proposed feature selection technique consists of nonnegative matrix factorization to decompose the data into feature weight matrix and representation matrix, inner product norm as regularization for both feature weight matrix and representation matrix, adaptive structure learning to preserve local information and Schatten-p norm as rank constraint. To demonstrate the effectiveness of the proposed method, numerical studies are conducted on six benchmark microarray datasets. The results show that the proposed technique outperforms eight state-of-art unsupervised feature selection techniques in terms of clustering accuracy and normalized mutual information.

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.
 Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.
 Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.
 OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.
 METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.
 RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.
 CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Early stage autism detection using ANFIS and extreme learning machine algorithm

The requisite of detecting Autism in the initial stage proposed dataset is exceptionally high in the recent era since it affects children with severe impacts on social and communication developments by damaging the neural system in a broader range. Thus, it is highly essential to identify this Autism in the primary stage. So many methods are employed in autism detection but fail to produce accurate results. Therefore, the present study uses the data mining technique in the process of autism detection, which provides multiple beneficial impacts with high accuracy as it identifies the essential genes and gene sequences in a gene expression microarray dataset. For optimally selecting the genes, the Artificial Bee Colony (ABC) Algorithm is utilized in this study. In contrast, the feature selection process is carried out by five different algorithms: tabu search, correlation, information gain ratio, simulated annealing, and chi-square. The proposed work utilizes a hybrid Extreme Learning Machine (ELM) algorithm based Adaptive Neuro-Fuzzy Inference System (ANFIS) in the classification process, significantly assisting in attaining high-accuracy results. The entire work is validated through Java. The obtained outcomes have specified that the introduced approach provides efficient results with an optimal precision value of 89%, an accuracy of 93%, and a recall value of 87%.

VNN1 as a potential biomarker for sepsis diagnosis and its implications in immune infiltration and tumor prognosis.

This study explored novel biomarkers for diagnosing sepsis, a severe disease prevalent in clinical settings, particularly threatening to elderly patients. Using microarray gene expression datasets and fatty acid metabolism signatures, we identified differentially expressed genes between sepsis and healthy control groups. Correlations between candidate genes, immune cells, and immune function were assessed. Logistic regression analysis and single-gene GSEA analysis were performed to identify potential biomarkers. The biomarkers' association with different types of tumors was investigated. Twelve genes related to fatty acid metabolism were excluded. CA4, OLAN, and VNN1 were found relevant to immune cells and function. Among these, only VNN1 showed statistical significance (p < 0.05), with a strong area under the ROC curve (0.995). High VNN1 expression indicated activation of certain metabolic pathways, while low expression suggested potential autoimmune responses. VNN1 was up-regulated in eight tumors and down-regulated in eight others. High VNN1 expression was linked to poor prognosis in six types of tumors, and low expression was linked to poor prognosis in four types of tumors. VNN1 expression showed correlations with stromal scores, immune scores, and cancer purity in different types of tumors. VNN1 holds promise as a potential biomarker for sepsis diagnosis and is significant in identifying immune infiltration in tumor tissue and predicting tumor prognosis.

In silico analysis and experimental validation shows negative correlation between miR-1183 and cell cycle progression gene 1 expression in colorectal cancer.

MicroRNAs (miRNAs) are small noncoding RNAs that post-transcriptionally regulate gene expression by binding to the 3' untranslated regions (UTR) of target genes. Aberrant expression of miRNAs can lead to disease, including cancer. Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Among several factors, differential expression of miRNA can have serious consequences on disease progression. This study was designed to computationally identify and experimentally verify strong miRNA candidates that could influence CRC progression. In silico analysis of publicly available gene expression microarray datasets revealed significant upregulation of miR-1183 in CRC. Comparison of mRNA microarray expression data with predicted miR-1183 targets led to the identification of cell cycle progression gene 1 (CCPG1) as strong, negatively correlated miR-1183 target. Expression analysis by means of quantitative PCR validated the inverse correlation between miR-1183 and CCPG1 in colorectal cancer tissues. CCPG1 indirectly modulates the cell cycle by interacting with the PH/DH domain of Dbs (Rho-specific guanine nucleotide exchange factor). Interestingly, the computational analysis also showed that miR-1183 is upregulated in liver and gastric cancer. This finding is notable as the liver and stomach are the primary metastatic sites for colorectal cancer and hepatocellular carcinoma respectively. This novel finding highlights the broader implications of miR-1183 dysregulation beyond primary CRC, potentially serving as a valuable prognostic marker and a therapeutic target for both primary and metastatic CRC.

A comprehensive identification of potential molecular targets and small drugs candidate for melanoma cancer using bioinformatics and network-based screening approach

Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein–protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs’ biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments. Communicated by Ramaswamy H. Sarma

Crosstalk between TRPV1 and immune regulation in Fuchs endothelial corneal dystrophy

Fuchs endothelial corneal dystrophy (FECD) is the leading indication for corneal transplantation worldwide. Our aim was to investigate the role of transient receptor potential vanilloid subtype 1 (TRPV1) and the associated immune regulation contributing to this pathological condition. Significant upregulation of TRPV1 was detected in the H2O2-induced in vitro FECD model. Based on gene expression microarray dataset GSE142538 and in vitro results, a comprehensive immune landscape was studied and a negative correlation was found between TRPV1 with different immune cells, especially regulatory T cells (Tregs). Functional analyses of the 313 TRPV1-related differentially expressed genes (DEGs) revealed the involvement of TRP-regulated calcium transport, as well as inflammatory and immune pathways. Four TRPV1-related core genes (MAPK14, GNB1, GNAQ, and ARRB2) were screened, validated by microarray dataset GSE112039 and the combined validation dataset E-GEAD-399 & 564, and verified by in vitro experiments. Our study suggested a potential crosstalk between TRPV1 and immune regulation contributing to FECD pathogenesis. The identified pivotal biomarkers and immune-related pathways provide a novel framework for future mechanistic and therapeutic studies of FECD.

ProJect: a powerful mixed-model missing value imputation method.

Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect.

1464-P: Identification of Candidate Biomarkers for Type 1 Diabetes Mellitus by Bioinformatics Analysis of Pooled Microarray Gene Expression Datasets in Gene Expression Omnibus

Background: Type 1 diabetes (T1DM) is a serious threat to childhood life and has a complicated pathogenesis. Currently, molecular mechanisms of T1DM remain largely unclear. The aim of this study was to identify the candidate genes in T1DM by integrated bioinformatics analysis. Methods: Transcriptomic datasets (GSE156035) in the GEO database were analyzed for differentially expressed genes (DEGs) using the R statistical language. The differentially expressed genes (DEGs) were identified, and the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A protein - protein interaction (PPI) network was applied to screen out the candidate genes. Results: The results revealed that 273 DEGs of the three datasets were ascertained in our study, including 135 upregulated genes and 138 downregulated genes. The GO and KEGG enrichment analysis results showed that the functions of DEGs mainly involved in regulation of transcription from RNA polymerase II promoter, specific granule lumen, transcriptional activator activity, Osteoclast differentiation pathway, etc. Through the PPI analysis network, the core genes with the highest degree of 6 nodes were selected: FOS, RHOA, CXCL8, FOSB, EGR1, and DUSP1. Conclusion: The genes, identified in this study, may play a vital regulatory role in the occurrence and development of T1DM. Also, they are closely related to obesity and diabetes mellitus. Our results provide novel biomarkers that could be used as representative reference indicators or potential therapeutic targets for T1DM clinical applications. Disclosure K.Feng: None. Funding SBHS (2022-16)