Gene Expression Profiling Datasets Research Articles

Identification of potential biomarkers and drug of ischemic stroke in patients with COVID-19 through machine learning

The relationship between COVID-19 and ischemic stroke (IS) has attracted significant attention, yet the precise mechanism at the gene level remains unclear. This study aims to reveal potential biomarkers and drugs for COVID-19-related IS through bioinformatics methods. We collected two gene expression profiling datasets, GSE16561 and GSE213313, and selected GSE179879 and GSE196822 as validation sets for analysis. Through analysis, we identified 77 differentially expressed genes (DEGs) shared between COVID-19 and IS. Further gene enrichment analysis revealed that these genes are primarily involved in immune regulation. By constructing a protein-protein interaction network, we screened out nine hub genes, including FCGR3A, KLRB1, IL2RB, CD2, IL7R, CCR7, CD3D, GZMK, and ITK. In LASSO regression analysis, we evaluated the ROC curve's area under the curve (AUC) scores of key genes to assess their diagnostic accuracy. Subsequently, we performed random forest (RF), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and neural network construction on hub genes to ensure accurate diagnosis of IS. Finally, by intersecting the results of three algorithms (LASSO regression, random forest, and SVM), CD3D and ITK were identified as the ultimate key genes. Based on this, we predicted potential targeted drug Blinatumomab. These research findings provide clues for a deeper understanding of the biological mechanisms of COVID-19-related IS and offer new insights for exploring novel treatment approaches.

High WEE1 expression is independently linked to poor survival in multiple myeloma.

Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. We therefore analyzed the MMRF CoMMpass dataset (N=659) and identified a high-risk group (top tertile) and a low-risk group ( bottom tertile) based on WEE1 expression sorted in descending order. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M-checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but has not until this time been implicated in MM. PFS was significantly different (p <1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N=341) and 3 (N=214) trials. Our results show WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic pathways.

A nomoscore of four genes for predicting the rupture risk in abdominal aortic aneurysm patients with osteoarthritis

BackgroundAbdominal aortic aneurysm (AAA) represents one of the most life-threatening cardiovascular diseases and is increasingly becoming a significant global public health concern. The aneurysms-osteoarthritis syndrome (AOS) has gained recognition, as patients with this syndrome often exhibit early-stage osteoarthritis (OA) and have a substantially increased risk of rupture, even with mild dilation of the aneurysm. The aim of this study was to discover potential biomarkers that can predict the occurrence of AAA rupture in patients with OA. MethodsTwo gene expression profile datasets (GSE98278, GSE51588) and two single-cell RNA-seq datasets (GSE164678, GSE152583) were obtained from the GEO database. Functional enrichment analysis, PPI network construction, and machine learning algorithms, including LASSO, Random Forest, and SVM-RFE, were utilized to identify hub genes. In addition, a nomogram and ROC curves were generated to predict the risk of rupture in patients with AAA. Moreover, we analyzed the immune cell infiltration in the AAA tissue microenvironment by CIBERSORT and validated key gene expression in different macrophage subtypes through single-cell analysis. ResultsA total of 105 intersecting DEGs that showed consistent changes between rAAA and OA dataset were identified. From these DEGs, four hub genes (PAK1, FCGR1B, LOX and PDPN) were selected by machine learning. High predictive performance was observed for the nomogram based on these hub genes, with an AUC of 0.975 (95 % CI: 0.942–1.000). Abnormal immune cell infiltration was detected in rAAA and correlated significantly with the hub genes. Ruptured AAA cases exhibited higher nomoscore values and lower M2 macrophage infiltration compared to stable AAA. Validation in animal models (PPE+BAPN-induced rAAA) confirmed the significant role of these biomarkers in AAA pathology. ConclusionThe present study successfully identified four potential hub genes (PAK1, FCGR1B, LOX and PDPN) and developed a robust predictive nomogram to assess the risk of AAA rupture. The findings also shed light on the connection between hub genes and immune cell components in the microenvironment of rAAA. These findings support future research on key genes in AAA patients with OA, providing insights for novel management strategies for AAA.

Development and validation of a hypoxia- and mitochondrial dysfunction- related prognostic model based on integrated single-cell and bulk RNA sequencing analyses in gastric cancer.

Gastric cancer (GC) remains a major global health threat ranking as the fifth most prevalent cancer. Hypoxia, a characteristic feature of solid tumors, significantly contributes to the malignant progression of GC. Mitochondria are the major target of hypoxic injury that promotes mitochondrial dysfunction during the development of cancers including GC. However, the gene signature and prognostic model based on hypoxia- and mitochondrial dysfunction-related genes (HMDRGs) in the prediction of GC prognosis have not yet been established. The gene expression profile datasets of stomach cancer patients were retrieved from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Prognostic genes were selected using Least Absolute Shrinkage andSelection Operator Cox (LASSO-Cox) regression analysis to construct a prognostic model. Immune infiltration was evaluated through ESTIMATE, CIBERSORT, and ssGSEA analyses. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were utilized to explore implications for immunotherapy. Furthermore, in vitro experiments were conducted to validate the functional roles of HMDRGs in GC cell malignancy. In this study, five HMDRGs (ZFP36, SERPINE1, DUSP1, CAV1, and AKAP12) were identified for developing a prognostic model in GC. This model stratifies GC patients into high- and low-risk groups based on median risk scores. A nomogram predicting overall survival (OS) was constructed and showed consistent results with observed OS. Immune infiltration analysis indicated that individuals in the high-risk group tend to exhibit increased immune cell infiltration. Additionally, analysis of cancer immunotherapy responses revealed that high-risk group patients exhibit poorer responses to cancer immunotherapy compared to the low-risk group. Immunohistochemistry (IHC) staining indicated that the expression levels of HMDRGs were remarkably correlated with GC, of which, SERPINE1 displayed the most pronounced up-regulation, while ZFP36 exhibited the most notable down-regulation in GC patients. Furthermore, in vitro investigation validated that SERPINE1 and ZFP36 contribute to the malignant processes of GC cells correlated with mitochondrial dysfunction. This study presents a novel and efficient approach to evaluate GC prognosis and immunotherapy efficacy, and also provides insights into understanding the pathogenesis of GC.

Predicting the ER Status Using the Gene Expression Profiles

Breast cancer is the most frequently diagnosed malignancy among women globally, making it the leading cause of cancer incidence worldwide. This type of cancer is particularly challenging due to its high degree of molecular heterogeneity, with many different subtypes exhibiting distinct genetic and biological characteristics. Estrogen receptors (ERs) play a crucial role in breast cancer by acting as receptors for the hormone estrogen, often referred to as the hormone receptor (HR). The presence or absence of ERs, known as ER status, significantly impacts treatment decisions, making accurate prediction of ER status essential for personalized treatment plans. Various methods have been proposed in the literature to predict ER status based on gene expression profiles using microarray genome-wide gene expression profiling. The issue of class imbalance, indicated by a notable variation in the number of samples per class and the curse of dimensionality, is a significant concern with microarray datasets. This paper introduces a novel technique, Fuzzy XGBoost, to predict ER status in breast cancer cases while addressing the class imbalance problem. The proposed algorithm enhances the traditional XGBoost method by incorporating fuzzy logic principles, improving its robustness, computational power, and accuracy in handling imbalanced datasets. Our research demonstrates the effectiveness of the proposed Fuzzy XGBoost algorithm in accurately predicting ER status across multiple gene expression profile datasets. This approach not only contributes to advancements in breast cancer treatment but also showcases the power of computational methods in bioinformatics. We evaluate our method using four datasets: GSE2990, GSE3494, GSE6532, and GSE7390, achieving predictive accuracies of 100%, 92%, 100%, and 96%, respectively. The successful application of Fuzzy XGBoost in this context highlights its potential for broader use in bioinformatics, where leveraging computational power to address challenges such as imbalanced data is critical for predictive accuracy and personalized treatment. DOI: https://doi.org/10.52783/pst.619

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Integrative analysis of gene and microRNA expression profiles reveals candidate biomarkers and regulatory networks in psoriasis.

Psoriasis (PS) is a chronic inflammatory skin disease with a long course and tendency to recur, the pathogenesis of which is not fully understood. This article aims to identify the key differentially expressed genes (DEGs) and microRNA (miRNAs) of PS, construct the core miRNA-mRNA regulatory network, and investigate the underlying molecular mechanism through integrated bioinformatics approaches. Two gene expression profile datasets and 2 miRNA expression profile datasets were downloaded from the gene expression omnibus (GEO) database and analyzed by GEO2R. Intersection DEGs and intersection differentially expressed miRNAs (DEMs) were each screened. The Metascape database and R software were used to perform enrichment analysis of intersecting DEGs and study their functions. Target genes of DEMs were predicted from the online database miRNet. The protein-protein interaction files of the overlapping target genes were obtained from string and the miRNA-mRNA network was constructed by Cytoscape software. In addition, the online web tool CIBERSORT was used to analyze the immune infiltration of dataset GSE166388, and the relative abundance of 22 immune cells in the diseased and normal control tissues was calculated and assessed. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to verify the relative expression of the screened miRNAs and mRNAs to assess the applicability of DEMs and DEGs as biomarkers in PS. A total of 205 mating DEGs and 6 mating DEMs were screened. 103 dysregulated crossover genes from 205 crossover DEGs and 7878 miRNA target genes were identified. The miRNA-mRNA regulatory network was constructed and the top 10 elements were obtained from CytoHubba, including hsa-miR-146a-5p, hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-18a-5p, CDK1, CCNA2, CCNB1, MAD2L1, RRM2, and CCNB2. QRT-PCR revealed significant differences in miRNA and gene expression between inflammatory and normal states. In this study, the miRNA-mRNA core regulator pairs hsa-miR-146a-5p, hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-18a-5p, CDK1, CCNA2, CCNB1, MAD2L1, RRM2, and CCNB2 may be involved in the course of PS. This study provides new insights to discover new potential targets and biomarkers to further investigate the molecular mechanism of PS.

Robust and Effective: A Deep Matrix Factorization Framework for Classification.

For complex data, high dimension and high noise are challenging problems, and deep matrix factorization shows great potential in data dimensionality reduction. In this article, a novel robust and effective deep matrix factorization framework is proposed. This method constructs a dual-angle feature for single-modal gene data to improve the effectiveness and robustness, which can solve the problem of high-dimensional tumor classification. The proposed framework consists of three parts, deep matrix factorization, double-angle decomposition, and feature purification. First, a robust deep matrix factorization (RDMF) model is proposed in the feature learning, to enhance the classification stability and obtain better feature when faced with noisy data. Second, a double-angle feature (RDMF-DA) is designed by cascading the RDMF features with sparse features, which contains the more comprehensive information in gene data. Third, to avoid the influence of redundant genes on the representation ability, a gene selection method is proposed to purify the features by RDMF-DA, based on the principle of sparse representation (SR) and gene coexpression. Finally, the proposed algorithm is applied to the gene expression profiling datasets, and the performance of the algorithm is fully verified.

Exploring the pathogenesis and immunological profiles of psoriasis complicated with MASLD.

Both psoriasis and metabolic dysfunction-associated steatotic liver disease (MASLD) are immune-mediated chronic inflammatory diseases. Psoriasis manifests itself mainly as skin damage, while MASLD mainly involves the liver promoting liver fibrosis, which has a significant impact on patient health and quality of life. Some clinical studies have shown that there are mutually reinforcing mechanisms between these two diseases, but they are not clearly defined, and this paper aims to further explore their common pathogenesis. Gene expression profiling datasets (GSE30999, GSE48452) and single cell datasets (GSE151177, GSE186328) for psoriasis and MASLD were downloaded from the Gene Expression Omnibus (GEO) database. Common differential gene sets were obtained by gene differential analysis, and then functional enrichment of differential genes was performed to find associated transcription factors and PPI protein network analysis. Single-cell datasets were validated for gene expression and explored for cellular communication, gene set differential analysis and immune infiltration analysis. We identified seven common differential genes, all of which were upregulated.The IL-17 pathway, tumor necrosis factor (TNF-α) pathway were shown in strong association with both diseases, and five transcription factors regulating the differential genes were predicted. Two key genes (MMP9, CXCL10) and three key transcription factors (TF) (IRF1, STAT1, NFKB1) were obtained by PPI protein network analysis. Single cell dataset verified the expression of key genes, and combined with gene set differential analysis, immune infiltration revealed that CD4+ T cells, NK cells and macrophages were heavily infiltrated in both diseases. IL-17, IL-1 and cGAS-STING pathways were highly expressed in both diseases, and both diseases share a similar immune microenvironment. Our study reveals the common pathogenesis of psoriasis and MASLD from gene expression to immune cell similarities and differences, identifies key genes and regulatory pathways common to both, and elucidates the similarities in the immune microenvironment of both diseases, providing new ideas for subsequent studies on targeted therapy.

Abstract PO1-24-12: Investigating the immunological function of alpha-2-glycoprotein 1, zinc-binding in regulating tumor response in the breast cancer microenvironment

Abstract Purpose: Tumor immunology has attracted considerable attention as an innovative therapeutic strategy for various types of cancer. Elucidating the unique immunoregulatory mechanisms in the breast cancer microenvironment will assist in the development of novel treatment strategies. Recently, we and other researchers have found that androgen receptor (AR) expression is associated with the immunosuppressive phenotype in the breast cancer microenvironment, suggesting some immunoregulatory function in breast cancer; however, the mechanism remains unclear. Here, we focused on AR-dependent secreted protein, alpha-2-glycoprotein 1, zinc-binding (ZAG) encoded by the AZGP1 gene in breast cancer, which is structurally similar to HLA class I and is implicated in immune regulation. In this study, we investigated the immunological function of AZGP1/ZAG in the breast cancer microenvironment. Methods: We performed a gene set enrichment analysis (GSEA) to screen the biological processes associated with AZGP1 expression using a gene expression profile dataset of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Subsequently, we analyzed the correlation between AZGP1 expression and the immune cell composition in breast cancer tissues estimated with the CIBERSORTx using METABRIC and another dataset of The Sweden Cancerome Analysis Network-Breast. In our previous study of 45 breast cancer tissue samples, we evaluated the infiltration of 11 types of immune cells using flow cytometry (FCM). ZAG expression was further evaluated by immunohistochemistry, and the relationship between ZAG expression and the immune cell composition in breast cancer tissue was analyzed. Based on the results of this analysis (shown in the results section) we hypothesized that ZAG has some effect on the macrophage (Mφ). The action of ZAG in M1/M2 polarization models constructed using primary culture of human peripheral blood mononuclear cells (PBMC)-derived Mφ was assessed based on the expression of M1/M2 differentiation markers (CD86, CD80/CD163, MRC1) and HLA class I/II expression evaluated by FCM (n=15 each). Results: GSEA demonstrated that AZGP1 expression was negatively correlated with multiple gene sets representing immunological processes, including inflammatory response, allograft rejection, interferon gamma response, IL6/JAK/STAT3 signaling, complement, and IL2/STAT5 signaling. Analysis by CIBERSORTx showed that AZGP1 expression was negatively correlated with the absolute score (the absolute abundance of total immune cell infiltration), Mφ M1, NK cells activated, CD4+ T memory activated, and CD8+ T (r&amp;lt;-3 and p&amp;lt; 0.05). Analyses of our in-house dataset using breast cancer tissue showed that ZAG expression was associated with decreased infiltration of monocytes/macrophages, non-classical monocytes, and myeloid-derived suppressor cells in breast cancer tissues assessed by FCM. In the in vitro analyses, ZAG decreased the expression of CD80, CD163, MRC1, and HLA classes I and II in the M1 polarization model and the expression of CD163 and MRC1 in the M2 polarization model. Conclusion: AZGP1/ZAG was associated with an immunosuppressive phenotype and reduced infiltration of specific immune cell subsets, particularly Mφ, into breast cancer tissues. In the in vitro analysis, ZAG demonstrated some regulatory effects on the phenotypic change of Mφ. Our findings strongly suggest ZAG is a novel mediator of AR-dependent immunomodulation in the breast cancer microenvironment, laying the foundation for future studies to elucidate the immunological role of ZAG in breast cancer. Citation Format: Toru Hanamura, Kozue Yokoyama, Shigehisa Kitano, Hiroshi Kagamu, Makiko Yamashita, Mayako Terao, Takuho Okamura, Nobue Kumaki, Katsuto Hozumi, Takayuki Iwamoto, Chikako Honda, Sasagu Kurozumi, Jennifer Richer, Naoki Niikura. Investigating the immunological function of alpha-2-glycoprotein 1, zinc-binding in regulating tumor response in the breast cancer microenvironment [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 PO1-24-12.

Abstract PO2-06-08: Suppression of GATA2/3-FOXA1-HER3 Axis by Histone Deacetylase (HDAC) Inhibitors shows Antitumor Activity in Basal-like Breast Cancer

Abstract Introduction: Basal-like breast cancer (BLBC) represents an aggressive subtype of triple-negative breast cancer (TNBC) that exhibits a high risk of recurrence and resistance to treatment. Histone deacetylase (HDAC) inhibitors (HDACis), including panobinostat and romidepsin, have obtained approval for the treatment of hematopoietic malignancies and demonstrated effectiveness in TNBC cells. We recently found that panobinostat and romidepsin potently induced TNBC cell growth inhibition and apoptosis via downregulation of HER3 through suppression of forkhead box protein A1 (FOXA1), a pioneering transcription factor. Nonetheless, the underlying mechanism through which HDACis suppress FOXA1, thereby inhibiting HER3 expression and its downstream signaling in FOXA1/HER3 co-expressing TNBC remains elusive. Methods: Colony formation, MTS, and LIVE/DEAD cell staining assays were used to detect cell viability. Apoptosis was detected by flow cytometry assays. QRT-PCR, western blots, and immunohistochemistry were performed to determine the expression and activation of genes and/or proteins. Co-immunoprecipitation was performed to assess the interaction between proteins. Lentivirus vectors containing cDNA or shRNAs were used to overexpress or knockdown gene expression. Chromatin immunoprecipitation-quantitative PCR and dual luciferase reporter assays were performed to elucidate the regulatory role of gene transcription. Results: Elevated expression of FOXA1 was observed in BLBC specimens and cell lines tested. FOXA1 was co-expressed with HER3 and transcriptionally activated the HER3 expression in BLBC cells. HER3 formed heterodimers with epidermal growth factor receptor (EGFR), activating downstream signaling pathways in BLBC cells. Treatment with panobinostat and romidepsin resulted in growth inhibition and apoptosis in BLBC cells by downregulating FOXA1 expression and inhibiting HER3 signaling. Notably, the combination of HDACis with an EGFR inhibitor (gefitinib) or an anti-HER3 antibody synergistically enhanced the anti-survival effects on BLBC cells. Additionally, gene expression profiling datasets revealed a significant positive correlation between FOXA1 expression and the transcription factors GATA-Binding Protein 2/3 (GATA2/3). Patients with high GATA2/3-FOXA1 expression exhibited worse Relapse-Free Survival (RFS) compared to those with low expressions in BLBC via Kaplan-Meier analysis. Further investigations showed that GATA2/3 directly activated FOXA1 transcription by binding to its promoter. Moreover, ectopic expression of GATA2/3 not only restored FOXA1 expression downregulated by HDACis but also attenuated the anti-survival effects of HDACis on BLBC cells. Conclusion: HDACis exhibit potent inhibitory effects on BLBC cells via downregulation of GATA2/3-mediated repression of FOXA1 gene transcription, which in turn suppresses HER3 expression and signaling. Our findings indicate that epigenetic targeting of the GATA2/3-FOXA1-HER3 axis may be an effective therapeutic strategy for the eradication of BLBC tumors. Keywords: FOXA1, GATA2/3, HER3, HDAC inhibitors, Basal-like breast cancer Citation Format: Bolin Liu, Congcong Tan, Hui Lyu. Suppression of GATA2/3-FOXA1-HER3 Axis by Histone Deacetylase (HDAC) Inhibitors shows Antitumor Activity in Basal-like 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 PO2-06-08.

DNA methyltransferases-associated long non-coding RNA PRKCQ-AS1 regulate DNA methylation in myelodysplastic syndrome.

Myelodysplastic syndrome (MDS) is a group of clonal hematopoietic stem cell disorders. DNA hypermethylation is considered to be the key mechanism of pathogenesis for MDS. Studies have demonstrated that DNA methylation can be regulated by the co-effect between long non-coding RNAs (lncRNAs) and DNA methyltransferases (DNMTs). The aim of this study was to identify DNMTs-associated differentially expressed (DE) lncRNAs, which may be a novel diagnostic and therapeutic target for MDS. Two gene expression profile datasets (GSE4619 and GSE19429) were downloaded from the Gene Expression Omnibus (GEO) database. Systematic bioinformatics analysis was conducted. Then we verified the expression of PRKCQ-AS1 in MDS patients and features in SKM-1 cells. Bioinformatics analysis revealed that the DNMT-associated DE-lncRNA PRKCQ-AS1 was functionally related to DNA methylation. The target genes of PRKCQ-AS1 associated with DNA methylation are mainly methionine synthetase (MTR) and ten-eleven-translocation 1 (TET1). Moreover, the high expression of PRKCQ-AS1 was verified in real MDS cases. Further cellular analysis in SKM-1 cells revealed that overexpressed PRKCQ-AS1 promoted methylation levels of long interspersed nuclear element 1 (LINE-1) and cell proliferation, and apparently elevated both mRNA and protein levels of MTR and TET1, while knockdown of PRKCQ-AS1 showed opposite trend in SKM-1 cells. DNMT-associated DE-lncRNA PRKCQ-AS1 may affects DNA methylation levels by regulating MTR and TET1.

Boosted unsupervised feature selection for tumor gene expression profiles

AbstractIn an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples. Besides, it is difficult for these methods to guide the feature selection process by multiple feature subspaces comprehensively. In order to address these problems, a boosted unsupervised feature selection method (BoostUFS) is proposed for tumour gene expression profiles. Specifically, the authors design a boosting scheme to sequentially learn multiple compressed feature subspaces by focusing on ambiguous samples. The uncertainty of samples and the confidence of feature subspaces can be evaluated adaptively by minimising the overall loss of feature subspaces learning. Furthermore, we provide a consensus objective function with L2,1‐norm regularisation to combine these weighted feature subspaces and select discriminative features. Extensive experiments on several real‐world datasets of tumour gene expression profiles are carried out to demonstrate the effectiveness of the proposed method.

A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO

Feature selection can be seen as a multi-objective task, where the goal is to select a subset of features that exhibit minimal correlation among themselves while maximizing their correlation with the target label. Multi-objective particle swarm optimization algorithm (MOPSO) has been extensively utilized for feature selection and has achieved good performance. However, most MOPSO-based feature selection methods are random and lack knowledge guidance in the initialization process, ignoring certain valuable prior information in the feature data, which may lead to the generated initial population being far from the true Pareto front (PF) and influence the population’s rate of convergence. Additionally, MOPSO has a propensity to become stuck in local optima during the later iterations. In this paper, a novel feature selection method (fMOPSO-FS) is proposed. Firstly, with the aim of improving the initial solution quality and fostering the interpretability of the selected features, a novel initialization strategy that incorporates prior information during the initialization process of the particle swarm is proposed. Furthermore, an adaptive hybrid mutation strategy is proposed to avoid the particle swarm from getting stuck in local optima and to further leverage prior information. The experimental results demonstrate the superior performance of the proposed algorithm compared to the comparison algorithms. It yields a superior feature subset on nine UCI benchmark datasets and six gene expression profile datasets.

Predictive Factors for Methadone Maintenance Treatment Compliance: Exploring Resistance and Tolerance in Heroin Addiction

Background: Methadone maintenance treatment (MMT) has been a cornerstone in heroin addiction management. However, its efficacy varies among individuals. The complex interplay of genetic backgrounds and demographic data could influence the response to MMT in heroin addiction. No previous adoption study has aimed to merge these findings into a potential pre-treatment screening tool. Objectives: This study aimed to investigate the combined influence of dopamine and opioid receptors and receptor endocytosis machinery genes, individual genetic backgrounds, and demographic data on the response to MMT in patients with heroin addiction. Methods: We enrolled 80 heroin addicts receiving MMT for 3 months alongside 80 healthy individuals in a comparative study. The approach utilized multinomial, linear, and binary logistic regression analyses to investigate the interplay of genetic factors (DRD1-5, opioid receptors [µ1, δ1, and κ1], DNM1L, RAB22A, and COMT), demographic independent variables, including, family history, heroin duration, age onset, heroin dose, and methadone dose, and clinical markers Subjective Opiate Withdrawal Scale (SOWS) with compliance with MMT protocols. Results: Results revealed that a positive family history and a higher level of heroin dose significantly predicted poor compliance to MMT. Additionally, the patients with lower expression levels of DRD2 and higher expression levels of DNM1L and COMT genes were at higher risk for poor compliance with the treatment. Conclusions: By utilizing a comprehensive dataset of gene expression profiles and demographic and clinical parameters, this study developed a regression model predicting resistance or response to methadone. This innovative approach seeks to bridge the gap between pharmacogenomics and clinical practice and offer a potential pre-treatment screening tool for personalized MMT strategies in opioid addiction management. The obtained findings hold intriguing promise for future research, potentially unlocking deeper insights into the underlying risk factors of addiction.

Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines.

Solar lentigines (SLs), serving as a prevalent characteristic of skin photoaging, present as cutaneous aberrant pigmentation. However, the underlying pathogenesis remains unclear and there is a dearth of reliable diagnostic biomarkers. The aim of this study was to identify diagnostic biomarkers for SLs and reveal its immunological features. In this study, gene expression profiling datasets (GSE192564 and GSE192565) of SLs were obtained from the GEO database. The GSE192564 was used as the training group for screening of differentially expressed genes (DEGs) and subsequent depth analysis. Gene set enrichment analysis (GSEA) was employed to explore the biological states associated with SLs. The weighted gene co-expression network analysis (WGCNA) was employed to identify the significant modules and hub genes. Then, the feature genes were further screened by the overlapping of hub genes and up-regulated differential genes. Subsequently, an artificial neural network was constructed for identifying SLs samples. The GSE192565 was used as the test group for validation of feature genes expression level and the model's classification performance. Furthermore, we conducted immune cell infiltration analysis to reveal the immune infiltration landscape of SLs. The 9 feature genes were identified as diagnostic biomarkers for SLs in this study. And an artificial neural network based on diagnostic biomarkers was successfully constructed for identification of SLs. GSEA highlighted potential role of immune system in pathogenesis of SLs. SLs samples had a higher proportion of several immune cells, including activated CD8 T cell, dendritic cell, myeloid-derived suppressor cell and so on. And diagnostic biomarkers exhibited a strong relationship with the infiltration of most immune cells. Our study identified diagnostic biomarkers for SLs and explored its immunological features, enhancing the comprehension of its pathogenesis.

Machine learning-based analysis of Ebola virus' impact on gene expression in nonhuman primates.

This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a novel machine learning-based approach for analyzing gene expression data from non-human primates (NHPs) infected with Ebola virus (EBOV). By focusing on host-pathogen interactions, this research aims to enhance the understanding and identification of critical biomarkers for Ebola infection. We utilized a comprehensive dataset of NanoString gene expression profiles from Ebola-infected NHPs. The SMAS system combines gene selection based on both statistical significance and expression changes. Employing linear classifiers such as logistic regression, the method facilitates precise differentiation between RT-qPCR positive and negative NHP samples. The application of SMAS led to the identification of IFI6 and IFI27 as key biomarkers, which demonstrated perfect predictive performance with 100% accuracy and optimal Area Under the Curve (AUC) metrics in classifying various stages of Ebola infection. Additionally, genes including MX1, OAS1, and ISG15 were significantly upregulated, underscoring their vital roles in the immune response to EBOV. Gene Ontology (GO) analysis further elucidated the involvement of these genes in critical biological processes and immune response pathways, reinforcing their significance in Ebola pathogenesis. Our findings highlight the efficacy of the SMAS methodology in revealing complex genetic interactions and response mechanisms, which are essential for advancing the development of diagnostic tools and therapeutic strategies. This study provides valuable insights into EBOV pathogenesis, demonstrating the potential of SMAS to enhance the precision of diagnostics and interventions for Ebola and other viral infections.

Transcriptomic analysis of mRNAs in human whole blood identified age-specific changes in healthy individuals.

Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.

Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis

BackgroundMultiple modes of cell death occur during the development of sepsis. Among these patterns, cuproptosis has recently been identified as a regulated form of cell death. However, its impact on the onset and progression of sepsis remains unclear. MethodWe screened a dataset of gene expression profiles from patients with sepsis using the GEO database. Survival analysis was performed to analyze the relationship between cuproptosis-related genes (CRGs) and prognosis. Hub genes were identified through univariate Cox regression analysis. The diagnostic value of hub genes in sepsis was tested in both training sets (GSE65682) and validation sets (GSE134347). To examine the association between hub genes and immune cells, single-sample gene set enrichment analysis (ssGSEA) and Pearson correlation analysis were employed. Additionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). ResultsIn sepsis, most CRGs were upregulated, with only DLD and MTF1 downregulated. High expression of three genes (GLE, LIAS, and PDHB) was associated with better prognosis, but only two hub genes (LIAS, PDHB) reached statistical significance. The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793–0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively. ssGSEA analysis revealed a lower number of immune cells in the sepsis group, and there was a correlation between immune cell population and CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and liver tissues, but up-regulation was observed in lung tissues. Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulated in renal tissues. ConclusionsCuproptosis is emerging as a significant factor in the development of sepsis. LIAS and PDHB, identified as potential diagnostic biomarkers for cuproptosis-associated sepsis, are believed to play crucial roles in the initiation and progression of cuproptosis-induced sepsis.

Inferred Gene Expression By Cell-Free DNA Profiling Allows Noninvasive Lymphoma Classification

Background : Current diagnostic criteria for mature B-cell tumors require a combination of histopathological, immunophenotypic, cytogenetic, and genetic evaluations on tissue biopsy specimens (Campo, Blood 2022; Alaggio, Leuk 2022). Despite this battery of techniques, substantial heterogeneity remains in specific subgroups that can be further resolved using additional molecular diagnostic methods, including gene expression profiling (GEP). For example, among aggressive B-cell tumors, a subset recognized as high-grade B-cell lymphomas can be identified as harboring MYC+BCL2 lesions by FISH (HGBCL-DH-BCL2). However, a significant remaining high-risk subset also have a characteristic tumor gene expression “double-hit signature” or “dark zone signature” (DZsig) (Ennishi, JCO 2018; Alduaij, Blood 2023). Yet, inadequate tissue specimens can limit the application of these techniques for some patients. Separately, the invasive nature of tissue biopsies and their attendant risks can preclude adequate diagnostic evaluations in some patients, and logistical barriers for obtaining surgical tissue specimens can result in diagnostic delays. To address these unmet needs, we tested the performance of a novel noninvasive strategy to detect and classify mature B-cell tumors using plasma cfDNA, with special attention to HGBCL-DH-BCL2 and DZsig. Methods : We used EPIC-Seq to infer expression levels for genes of interest, using cfDNA fragmentomic signals at their transcription start sites (Esfahani, Nat Biotech 2022). We first curated and catalogued genes from prior GEP studies (n=56) of &amp;gt;10k diverse lymphoid tumors profiled by various RNA techniques. We systematically prioritized genes recurrently identified in multiple studies of tumor-specific expression profiles as compared with other tumor types and normal tissues. We included canonical markers used for immunodiagnosis by IHC and FACS, as well as key lineage and differentiation markers of normal B and T cells, and other leukocyte subsets. Finally, we also included recurrently mutated genomic regions and fusion hotspots for quantifying ctDNA levels using variant allelic fractions (VAF). To validate performance of DZsig in FFPE for HGBCL-DH-BCL2, we analyzed 95 FFPE biopsies by FISH (20% HGBCL-DH-BCL2), IHC, and RNA-seq. We then analyzed 36 corresponding pretreatment cfDNA samples (28% HGBCL-DH-BCL2) and used EPIC-Seq to infer gene expression. Results: Starting from our curated catalogue, we designed a targeted lymphoid EPIC-Seq panel (1676 genes; 2.6 MB) to resolve 11 major mature lymphoid malignancies and their clinically relevant molecular subtypes (cHL, DLBCL, HGBCL, FL, MCL, BL, MZL, PMBCL, SLL/CLL, PTCL, and MM). We next applied this EPIC-Seq panel to profile cfDNA samples from 207 lymphoma patients, including cHL (n=113), DLBCL (n=66), FL (n=15), and MCL (n=13) as well as healthy adults to assess specificity. We tested histology specific signatures derived from several existing RNA GEP datasets for cHL from sorted HRS cells (~85k by scRNA-Seq) and for DLBCL (n=959), FL (n=635), and MCL (n=100) from bulk RNA-Seq. Using this approach, we found high correlations between EPIC-Seq signature scores from cfDNA and corresponding circulating tumor VAFs in each histology [cHL (R p=0.83), DLBCL (R p=0.83), FL (R p=0.87), MCL (R p=0.81); Fig A]. In addition, when measuring these tumor derived signatures in cfDNA, each histology was significantly distinguishable from healthy controls (AUCs: cHL 0.94, DLBCL 0.91, FL 0.77, MCL 0.96). We next evaluated the DZsig when measured in LBCL patients and comparing paired tumor and cfDNA samples. We first confirmed the performance of the DZsig for detection of HGBCL-DH-BCL2 in 95 FFPE tumors by RNA-Seq (p&amp;lt;0.0001, AUC 0.85). We then noninvasively measured the DZsig in plasma cfDNA by EPIC-Seq (cfDZsig) and found significant discrimination of HGBCL-DH-BCL2 and DLBCL (p=0.0088, AUC 0.78, Fig B). Conclusions: These results confirm that inferred gene expression by cfDNA profiling allows noninvasive diagnosis of multiple lymphoma subtypes, including classification of challenging diagnostic entities such as HGBCL and DZsig+ tumors. Our ability to design and validate a pan-lymphoid gene panel for detection and classification of several lymphoma subtypes suggests the promise of this approach for other diverse tumors as well as the refinement of cfDZsig using a larger cohort.