Raw Gene Expression Research Articles

Development of Compendium for Esophageal Squamous Cell Carcinoma.

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors

Identifying genes with prognostic significance that can act as biomarkers in solid tumors can help stratify patients and uncover novel therapy targets. Here, our goal was to expand our previous ranking analysis of survival-associated genes in various solid tumors to include colon cancer specimens with available transcriptomic and clinical data. A Gene Expression Omnibus search was performed to identify available datasets with clinical data and raw gene expression measurements. A combined database was set up and integrated into our Kaplan-Meier plotter, making it possible to identify genes with expression changes linked to altered survival. As a demonstration of the utility of the platform, the most powerful genes linked to overall survival in colon cancer were identified using uni- and multivariate Cox regression analysis. The combined colon cancer database includes 2,137 tumor samples from 17 independent cohorts. The most significant genes associated with relapse-free survival with a false discovery rate below 1% in colon cancer carcinoma were RBPMS (hazard rate [HR]= 2.52), TIMP1 (HR= 2.44), and COL4A2 (HR= 2.36). The three strongest genes associated with shorter survival in stage II colon cancer include CSF1R (HR= 2.86), FLNA (HR= 2.88), and TPBG (HR= 2.65). In summary, a new integrated database for colon cancer is presented. A colon cancer analysis subsystem was integrated into our Kaplan-Meier plotter that can be used to mine the entire database (https://www.kmplot.com). The portal has the potential to be employed for the identification and prioritization of promising biomarkers and therapeutic target candidates in multiple solid tumors including, among others, breast, lung, ovarian, gastric, pancreatic, and colon cancers.

The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas.

Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

Abstract 7413: Establishing a database for the discovery and validation of survival-associated genes in colon cancer

Abstract Introduction: Unveiling the prognostic significance of genes as potential biomarkers in colon cancer (CRC) is crucial for the discovery of new therapeutic targets and the effective stratification of patients into clinically relevant groups. The primary objective of this study was to establish a transcriptomic cohort comprising clinical data from colon cancer specimens and to leverage this dataset to pinpoint genes with robust prognostic implications. Methods: Our approach involved a comprehensive search of the Gene Expression Omnibus (GEO) to identify datasets containing both clinical information and raw gene expression measurements for individual colon cancer specimens. Subsequently, we curated and integrated these datasets into a unified database, and linked into our Kaplan-Meier plotter. This integration enabled the identification of genes whose expression changes were associated with altered survival outcomes. The identification of the most influential genes in relation to overall survival in stage 2 and stage 3/4 tumors was accomplished through both uni- and multivariate Cox regression analyses. To mitigate the impact of multiple hypothesis testing, a false discovery rate was computed for each marker. Results: The database encompasses a total of 2,137 tumor samples derived from 17 distinct independent cohorts. Within this dataset, 1,343 samples are equipped with recorded relapse-free survival data, while 1,061 samples provide information on overall survival events along with the corresponding duration. In addition to survival-related metrics, the database encompasses a spectrum of supplementary clinical parameters such as gender, T (tumor size), N (lymph node involvement), M (metastasis), grade, stage, location, and microsatellite instability. The most significant genes associated with relapse-fee survival with FDR<1% in stage 2 CRC include CSF1R (HR=2.86), FLNA (HR=2.88), TPBG (HR=2.65), B2M (HR=2.62) and LOXL2 (HR=2.63). When ordered by the Cox regression p-value in stage 3 and 4 tumors, the five strongest genes were BHLHE40 (HR=2.6), COL4A2 (HR=2.02), TSC22D3 (HR=1.95), NPR3 (HR=1.91), and A2M (HR=1.86). Conclusions: In this study, we successfully developed a robust integrated database dedicated to colon cancer. By leveraging this resource, we pinpointed the most impactful prognostic biomarkers and potential therapy targets specifically within stage 2 and stage 3/4 patients. Beyond its current applications, this database holds promise for future endeavors, serving as a valuable tool for identifying and prioritizing additional promising biomarkers and therapeutic targets in other sub-cohorts of colon cancer. The entire database can be utilized at www.kmplot.com. Citation Format: Balazs Gyorffy. Establishing a database for the discovery and validation of survival-associated genes in colon cancer [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 7413.

Highly Ordered Nanotube-Like Microstructure on Titanium Dental Implant Surface Fabricated via Anodization Enhanced Cell Adhesion and Migration of Human Gingival Fibroblasts.

Titanium (Ti) surface with nanotubes array via anodization has been used in dental implants to enhance bone regeneration but little research was carried out to evaluate whether the presence of highly ordered or disorderly distributed nanotubes array on titanium surface would have an effect on cell behaviors of gingival fibroblasts. The present study fabricated nanotubes arrays with varied topography under different constant voltage of electrochemical anodization in fluorine-containing electrolyte. Human gingival fibroblasts (HGFs) from extracted third molar were harvested and co-cultured with titanium disks with different nanotubes topography. Then cell behaviors of gingival fibroblasts including cell proliferation, adhesive morphology and cell migration were estimated to investigate the influence of titanium nanotubes on cell biology. Besides, gene and protein expression of adhesion molecule (integrin β1/β4/α6, fibronectin, intracellular adhesion molecule-1 and collagen type I) were detected to evaluate the influence of different surfaces on cell adhesion. Highly ordered arrays of nanotubes with pore diameter of 60 nm and 100 nm were fabricated under 30 and 40 V of anodization (TNT-30 and TNT-40) while disorderedly distributed nanotube arrays formed on the titanium surface under 50 V of anodization (TNT-50). Our results demonstrated that compared with raw titanium surface and disorderly nanotubes, surface with orderly nanotubes array increased cell area and aspect ratio, as well as cell migration ability in the early phase of cell adhesion (p<0.05). Besides, compared with raw titanium surface, gene and protein expression of adhesion molecules were upregulated in nanotubes groups to different extents, no matter whether in an orderly or disorderly array. Within the limitations of our study, we conclude that compared with raw titanium surface, the presence of nanotubes array on titanium surface could enhance cells adhesion and cell migration in the early phase. And compared with disorderly distributed nanotubes, highly ordered nanotubes array might provide a much more favorable surface for gingival fibroblasts to achieve a tight adhesion on the materials.

Identification and Validation of Genes Related to Macrophage Polarization and Cell Death Modes Under Mycobacterium tuberculosis Infection.

To investigate the correlation between M1/M2 macrophages (M1/M2 Mφ) and cell death mode under Mycobacterium tuberculosis (Mtb) infection. Raw gene expression profiles were collected from the Gene Expression Omnibus (GEO) database. Genes related to different cell death modes were collected from the KEGG, FerrDb and GSEA databases. The differentially expressed genes (DEGs) of the gene expression profiles were identified using the limma package in R. The intersection genes of M1/M2 Mφ with different cell death modes were obtained by the VennDiagram package. Hub genes were obtained by constructing the protein-protein interactions (PPI) network and Receiver Operating Characteristic (ROC) curve analysis. The expression of cell death modes marker genes and Hub genes were verified by Western Blot and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Bioinformatics analysis was performed to screen Hub genes of Mtb-infected M1 Mφ and different cell death modes, naming NFKB1, TNF, CFLAR, TBK1, IL6, RELA, SOCS1, AIM2; Hub genes of Mtb-infected M2 Mφ and different cell death modes, naming TNF, BIRC3, MAP1LC3C, DEPTOR, UVRAG, SOCS1. Combined with experimental validation, M1 Mφ under Mtb infection showed higher expression of death (including apoptosis, autophagy, ferroptosis, and pyroptosis) genes compared to M2 Mφ and genes such as NFKB1, TNF, CFLAR, TBK1, IL6, RELA, AIM2, BIRC3, DEPTOR show differential expression. NFKB1, TNF, CFLAR, TBK1, IL6, RELA, AIM2 in Mtb-infected M1 Mφ, and TNF, BIRC3, DEPTOR in Mtb-infected M2 Mφ might be used as potential diagnostic targets for TB. At early stage of Mtb infection, apoptosis, autophagy, ferroptosis, and pyroptosis occurred more significantly in M1 Mφ than that in M2 Mφ, which may contribute to the transition of Mtb-infected Mφ from M1-dominant to M2-dominant and contribute to the immune escape mechanisms of Mtb.

Gene expression profiling and protein–protein network analysis revealed prognostic hub biomarkers linking cancer risk in type 2 diabetic patients

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein–protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification

In the era of healthcare and its related research fields, the dimensionality problem of high-dimensional data is a massive challenge as it is crucial to identify significant genes while conducting research on diseases like cancer. As a result, studying new Machine Learning (ML) techniques for raw gene expression biomedical data is an important field of research. Disease detection, sample classification, and early disease prediction are all important analyses of high-dimensional biomedical data in the field of bioinformatics. Recently, machine-learning techniques have dramatically improved the analysis of high-dimension biomedical data sets. Nonetheless, researchers’ studies on biomedical data faced the challenge of vast dimensions, i.e., the vast features (genes) with a very low sample space. In this paper, two-dimensionality reduction methods, feature selection, and feature extraction are introduced with a systematic comparison of several dimension reduction techniques for the analysis of high-dimensional gene expression biomedical data. We presented a systematic review of some of the most popular nature-inspired algorithms and analyzed them. The paper is mainly focused on the original principles behind each of the algorithms and their applications for cancer classification and prediction from gene expression data. Lastly, the advantages and disadvantages of nature-inspired algorithms for biomedical data are evaluated. This review paper may guide researchers to choose the most effective algorithm for cancer classification and prediction for the satisfactory analysis of high-dimensional biomedical data.

Exploration and validation of the influence of angiogenesis-related factors in aortic valve calcification.

Over the years, bioinformatics tools have been used to identify functional genes. In the present study, bioinformatics analyses were conducted to explore the underlying molecular mechanisms of angiogenic factors in calcific aortic valve disease (CAVD). The raw gene expression profiles were from datasets GSE153555, GSE83453, and GSE51472, and the angiogenesis-related gene set was from the Gene Set Enrichment Analysis database (GSEA). In this study, R was used to screen for differentially expressed genes (DEGs) and co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) Pathway enrichment analysis were performed on DEGs and validated in clinical samples. DEGs in CAVD were significantly enriched in numerous immune response pathways, inflammatory response pathways and angiogenesis-related pathways. Nine highly expressed angiogenesis-related genes were identified, of which secretogranin II (SCG2) was the most critical gene. MiRNA and transcription factors (TFs) networks were established centered on five DEGs, and zinc finger E-box binding homeobox 1 (ZEB1) was the most important transcription factor, verified by PCR, immunohistochemical staining and western blotting experiments. Overall, this study identified key genes and TFs that may be involved in the pathogenesis of CAVD and may have promising applications in the treatment of CAVD.

Hsa_circ_0079557 Promotes the Proliferation of Colorectal Cancer Cells Through the hsa_circ_0079557/miR-502-5p/CCND1 Axis.

Recent studies have demonstrated the crucial regulatory roles of circular RNAs (circRNAs) in cancer initiation and progression. The sponge mechanism of circRNAs has been shown to be widely active in various types of tumors. However, many circRNAs still have not been verified to function through this mechanism. This study aimed to investigate the regulatory mechanism of hsa_circ_0079557 in colorectal cancer (CRC) and its role in CRC progression. Raw gene expression profile datasets were downloaded from Gene Expression Omnibus (GEO) and combined to form a new dataset. Hsa_circ_0079557 was found to be highly expressed in CRC. Its role was evaluated in vitro and in vivo through a series of experiments, including quantitative real-time polymerase chain reaction (qRT-PCR), flow cytometry, colony formation, cell counting kit-8 (CCK-8), transwell assays, scratch wound healing assays, nude mice experiments, and immunohistochemistry (IHC). The association between hsa_circ_0079557 and the identified target microRNAs (miRNA) was confirmed through fluorescence in situ hybridization (FISH) and dual-luciferase reporter assays. The downstream target proteins were predicted using the web-based tool "TargetScan," and their expressions were determined using Western blot (WB). Hsa_circ_0079557 was found to be relatively up-regulated in CRC tissues and cell lines. Suppression of hsa_circ_0079557 expression inhibited cell proliferation in vitro and in vivo. Additionally, hsa_circ_0079557 acted as a "molecular sponge" for miR-502-5p, up-regulating the expression of Cyclin D1 (CCND1). In this study, we identify a highly expressed circRNA in CRC and propose a novel pathway of hsa_circ_0079557/miR-502-5p/CCND1 in CRC.

The C-SHIFT Algorithm for Normalizing Covariances.

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods. We also evaluate the performance of C-SHIFT algorithm on real biological data.

A Microarray Data Pre-processing Method for Cancer Classification

The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of biological data to uncover significant patterns of interest. The exploitation of microarray data is of great challenge for many researchers. Raw gene expression data are usually vulnerable to missing values, noisy data, incomplete data, and inconsistent data. Hence, processing data before being applied for cancer classification is important. In order to extract the biological significance of microarray gene expression data, data pre-processing is a necessary step to obtain valuable information for further analysis and address important hypotheses. This study presents a detailed description of pre-processing data method for cancer classification. The proposed method consists of three phases: data cleaning, transformation, and filtering. The combination of GenePattern software tool and Rstudio was utilized to implement the proposed data pre-processing method. The proposed method was applied to six gene expression datasets: lung cancer dataset, stomach cancer dataset, liver cancer dataset, kidney cancer dataset, thyroid cancer dataset, and breast cancer dataset to demonstrate the feasibility of the proposed method for cancer classification. A comparison has been made to illustrate the differences between the dataset before and after data pre-processing.

1694P Single cell characterization of longitudinal biopsies from breast cancer patients treated with the aromatase inhibitor letrozole and the CDK4/6 inhibitor ribociclib

The recent introduction of CDK4/6 inhibitors has been one of the most pivotal breakthroughs in breast cancer therapy in the last decades. A growing body of evidence proposes that CDK4/6 inhibitors potentially influence the recruitment of immune cells in the tumor microenvironment, but also induce a senescent-like phenotype in the tumor cells. The NEOLETRIB-study is a multicenter, single-arm, open-label, neoadjuvant, phase II trial. Locally advanced breast cancer patients belonging to the luminal-A and luminal-B subtypes, received neoadjuvant therapy for 6 months with ribociclib (600 mg daily, 21 days on / 7 days off) and letrozole (2.5 mg daily). Pre-treatment, on-treatment (end of the first 21 days of therapy) and end-of-treatment biopsies were subjected to single-cell transcriptome using the Chromium Single-Cell v2 5′ Chemistry. Raw gene expression matrices were analyzed with the Seurat package (v4.0.2). First, empty droplets and droplets containing two cells were filtered out using the EmptyDrops and scDblFinder algorithms respectively. In addition, cell barcodes with < 500 UMIs, < 300 expressed genes, > 5000 expressed genes or > 15% of reads mapping to mitochondrial RNA were filtered out. The gene expression of the remaining good quality cells was normalized. Variably expressed genes were used to construct principal components (PCs). Significant PCs were further used to cluster cells. We identified 8 main cell types: T cells, B cells, epithelial cells, fibroblasts, endothelial cells, mast cells and dendritic cells. To discriminate between cancer and normal epithelial cells, we estimated for each epithelial cell the copy number variations using the InferCNV algorithm. Furthermore, to identify specific and specialized cell subtype, we clustered each of the eight cell type independently and annotated the clusters obtained using validated marker genes. We also compare our annotations with publish methods identifying cell states in single cell data (Luca et al., Cell 2021). We highlight how scRNA-seq and our analytic pipelines are allowing us to study the tumor microenvironment of breast cancer biopsies at a very high resolution.

Immune Gene Signature Expression Differs between African American and Caucasian Patients with Renal Cell Carcinoma

BACKGROUND: Predictive immune signatures such as the T-effector, the 26-gene “Renal 101 Immuno signature” and the 18-gene T-cell inflamed gene expression profile were developed in clinical trials enrolling predominantly Caucasians and there is a dearth of literature comparing tumor biology between African American (AA) and Caucasian patients. OBJECTIVE: To compare the immune gene signature expression in AA (n = 55) and Caucasian (n = 457) patients. METHODS: Raw gene expression count data were downloaded from the TCGA KIRC dataset and tumor samples from “white” and “black or AA” patients were selected. The gene expression values of the immune signatures were VST-transformed normalized counts and compared between the groups. RESULTS: There were 457 Caucasian and 55 AA patients in the TCGA. The immune gene expression in all three signatures was significantly lower in AA patients compared to Caucasians (p &lt; 0.05). We validated our findings in an independent dataset using Nanostring Immune Profile Panel. Since the majority of AA tumors in TCGA were stage I (71%), we compared gene expression between stage I AA tumors (n = 39) with stage I Caucasian tumors (n = 220). Once again, the immune gene expression was significantly lower in AA patients compared to Caucasians (p &lt; 0.05), indicating differences in tumor biology between the races. CONCLUSIONS: Low expression of predictive immune gene signatures in AA compared to Caucasian patients indicates a possible difference in the biology of their tumors. Future studies are needed to validate our findings in other datasets and to study the predictive role of these signatures in AA patients.

Analysis of immune related gene expression profiles and immune cell components in patients with Barrett esophagus

Barrett's esophagus (BE) is a well-known precancerous condition of esophageal adenocarcinoma. However, the immune cells and immune related genes involved in BE development and progression are not fully understood. Therefore, our study attempted to investigate the roles of immune cells and immune related genes in BE patients. The raw gene expression data were downloaded from the GEO database. The limma package in R was used to screen differentially expressed genes (DEGs). Then we performed the least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses to screen key genes. The proportion of infiltrated immune cells was evaluated using the CIBERSORT algorithm between BE and normal esophagus (NE) samples. The spearman index was used to show the correlations of immune genes and immune cells. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of key genes in BE. A total of 103 differentially expressed immune-related genes were identified between BE samples and normal samples. Then, 7 genes (CD1A, LTF, FABP4, PGC, TCF7L2, INSR,SEMA3C) were obtained after Lasso analysis and RF modeling. CIBERSORT analysis revealed that resting CD4 T memory cells and gamma delta T cells were present at significantly lower levels in BE samples. Moreover, plasma cell and regulatory T cells were present at significantly higher levels in BE samples than in NE samples. INSR had the highest AUC values in ROC analysis. We identified 7 immune related genes and 4 different immune cells in our study, that may play vital roles in the occurrence and development of BE. Our findings improve the understanding of the molecular mechanisms of BE.

Identification of autophagy-related genes as potential biomarkers for type 1 diabetes mellitus.

BackgroundType 1 diabetes mellitus (T1DM) is a metabolic disease in which the autoimmune destruction of pancreatic islet β-cells occurs. This study sought to investigate the role of autophagy-related genes and immune cells in the development of T1DM.MethodsWe acquired the raw gene expression profiles of 302 T1DM and 422 normal control peripheral blood samples from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the Limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The Search Tool for the Retrieval of Interacting Genes/Proteins (https://string-db.org/) and Cytoscape autophagy genes were intersected with the DEGs for the immune cell analysis and the correlation analysis.ResultsA total of 568 DEGs were identified in the T1DM and normal samples, of which 301 were upregulated and 267 were downregulated. The results of the functional and pathway enrichment analyses showed that the DEGs were closely associated with autophagy and immunity. Member RAS oncogene family (RAB11A), protein tyrosine phosphatase non-receptor type 11, lamin A/C, heat shock protein70, heat shock protein family A member 4, cluster of differentiation 8A, caspase 3 (CASP3), exportin 1, proto-oncogene, non-receptor tyrosine kinase, SMAD family member 4, and sirtuin 1 (SIRT1) were located at the center of the protein-protein interaction network as the core genes. The peripheral blood T cells were more elevated in the T1DM subjects than the normal subjects. RAB11A, CASP3, and SIRT1 are autophagy-associated genes. RAB11A and CASP3 were positively correlated with most immune cells, while SIRT1 was negatively correlated with most immune cells.ConclusionsAutophagy-related genes (i.e., RAB11A, CASP3, and SIRT1) and immune cells (i.e., T and B cells) may play important regulatory roles in the development of T1DM. Our findings provide novel insights into and potential targets for T1DM prediction and treatment.

Early Alzheimer's Disease Detection Using Different Techniques Based on Microarray Data: A Review

Alzheimer's Disease (AD) is a degenerative disease of the brain that results in memory loss due to the death of brain cells. Alzheimer's disease is more common as people get older. Memory loss happens over time, and as a result, the person loses the ability to react appropriately to their surroundings. Microarray technology has emerged as a new trend in genetic research, with many researchers utilizing it to look at the changes in gene expression in particular organisms. Microarray experiments can be used in various ways in the medical field, including the prediction and detection of disease. Large amounts of unprocessed raw gene expression profiles sometimes contribute to computational and analytic difficulties, including selecting dataset features and classifying them into an appropriate group or class. The large dimensions, lesser sample size, and noise in gene expression data make it difficult to attain good Alzheimer classification accuracy using the entire collection of genes. The categorization process necessitates careful feature reduction. As a result, a comprehensive review of microarray Alzheimer's disease studies is presented in this paper, focusing on feature selection techniques.

Macrophage Paired Immunoglobulin-Like Receptor B Deficiency Promotes Peripheral Atherosclerosis in Apolipoprotein E–Deficient Mice

Background: Peripheral atherosclerotic disease (PAD) is the narrowing or blockage of arteries that supply blood to the lower limbs. Given its complex nature, bioinformatics can help identify crucial genes involved in the progression of peripheral atherosclerosis. Materials and Methods: Raw human gene expression data for 462 PAD arterial plaque and 23 normal arterial samples were obtained from the GEO database. The data was analyzed using an integrated, multi-layer approach involving differentially-expressed gene analysis, KEGG pathway analysis, GO term enrichment analysis, weighted gene correlation network analysis, and protein-protein interaction analysis. The monocyte/macrophage-expressed leukocyte immunoglobulin-like receptor B2 (LILRB2) was strongly associated with the human PAD phenotype. To explore the role of the murine LILRB2 homologue PirB in vivo, we created a myeloid-specific PirB-knockout Apoe −/− murine model of PAD (PirB MΦKO) to analyze femoral atherosclerotic burden, plaque features of vulnerability, and monocyte recruitment to femoral atherosclerotic lesions. The phenotypes of PirB MΦKO macrophages under various stimuli were also investigated in vitro. Results: PirB MΦKO mice displayed increased femoral atherogenesis, a more vulnerable plaque phenotype, and enhanced monocyte recruitment into lesions. PirB MΦKO macrophages showed enhanced pro-inflammatory responses and a shift toward M1 over M2 polarization under interferon-γ and oxidized LDL exposure. PirB MΦKO macrophages also displayed enhanced efferocytosis and reduced lipid efflux under lipid exposure. Conclusion: Macrophage PirB reduces peripheral atherosclerotic burden, stabilizes peripheral plaque composition, and suppresses macrophage accumulation in peripheral lesions. Macrophage PirB inhibits pro-inflammatory activation, inhibits efferocytosis, and promotes lipid efflux, characteristics critical to suppressing peripheral atherogenesis.

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods

BackgroundAlthough the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis.MethodsAll statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to identify differentially expressed genes (DEGs) between CAD samples and healthy controls. The WGCNA package was conducted to recognize CAD-related gene modules and hub genes, followed by recursive feature elimination analysis to select the optimal features genes (OFGs). The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. Further validation and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the classification performance.ResultsIn total, 374 DEGs, eight gene modules, 33 hub genes and 12 OFGs (HTR4, KISS1, CA12, CAMK2B, KLK2, DDC, CNGB1, DERL1, BCL6, LILRA2, HCK, MTF2) were identified. ROC curve analysis showed that the accuracy of SVM, RF and LR were 75.58%, 63.57% and 63.95% in validation; with area under the curve of 0.813 (95% confidence interval, 95% CI 0.761–0.866, P < 0.0001), 0.727 (95% CI 0.665–0.788, P < 0.0001) and 0.783 (95% CI 0.725–0.841, P < 0.0001), respectively.ConclusionsIn conclusion, this study found 12 gene signatures involved in the pathogenic mechanism of CAD. Among the CAD classifiers constructed by three machine learning methods, the SVM model has the best performance.