Gene Expression Profiling Datasets Research Articles

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<-3 and p< 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.

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.

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 >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<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.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species.

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thalianaand Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sitesand chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysisand Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

Interspecies co-expression analysis of lateral root development using inducible systems in rice, Medicago, and Arabidopsis.

Lateral roots are crucial for plant growth and development, making them an important target for research aiming to improve crop yields and food security. However, their endogenous ontogeny and, as it were, stochastic appearance challenge their study. Lateral Root Inducible Systems (LRIS) can be used to overcome these challenges by inducing lateral roots massively and synchronously. The combination of LRISs with transcriptomic approaches significantly advanced our insights in the molecular control of lateral root formation, in particular for Arabidopsis. Despite this success, LRISs have been underutilized for other plant species or for lateral root developmental stages later than the initiation. In this study, we developed and/or adapted LRISs in rice, Medicago, and Arabidopsis to perform RNA-sequencing during time courses that cover different developmental stages of lateral root formation and primordium development. As such, our study provides three extensive datasets of gene expression profiles during lateral root development in three different plant species. The three LRISs are highly effective but timing and spatial distribution of lateral root induction vary among the species. Detailed characterization of the stages in time and space in the respective species enabled an interspecies co-expression analysis to identify conserved players involved in lateral root development, as illustrated for the AUX/IAA and LBD gene families. Overall, our results provide a valuable resource to identify potentially conserved regulatory mechanisms in lateral root development, and as such will contribute to a better understanding of the complex regulatory network underlying lateral root development.

Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration.

Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease. The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed. A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively. ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.

Identification and experimental validation of autophagy-related genes in abdominal aortic aneurysm

AimAutophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis.MethodsTwo gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein–protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients.ResultsAccording to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip.ConclusionBioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Bioinformatical analysis of the key differentially expressed genes for screening potential biomarkers in Wilms tumor

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.

ENO2 as a Biomarker Regulating Energy Metabolism to Promote Tumor Progression in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and metastatic type of renal cell carcinoma. Despite significant advancements, the current diagnostic biomarkers for ccRCC lack the desired specificity and sensitivity, necessitating the identification of novel biomarkers and elucidation of their underlying mechanisms. Three gene expression profile datasets were obtained from the GEO database, and differentially expressed genes (DEGs) were screened. Gene Ontology and KEGG pathway analysis were conducted in ccRCC. To clarify the diagnosis and prognostic role of ENO2, Kaplan-Meier analysis and Cox proportional hazards regression analysis were performed. Functional experiments were also carried out to verify the significant role of ENO2 in ccRCC. Finally, tumor mutational burden analysis was utilized to investigate the potential role of ENO2 in gene mutations in ccRCC. The study showed that ENO2 is a potential biomarker for the diagnosis of ccRCC and can independently predict the clinical prognosis of ccRCC. Furthermore, we found that ENO2 can promote the occurrence and progression of ccRCC by affecting the glycolysis level of cells through the "Warburg effect". These findings provide new theories for the occurrence and development of ccRCC and can help formulate new strategies for its diagnosis and treatment.

Hypoxia-related genes and PD-L1 inhibitor resistance in hepatocellular carcinoma.

158 Background: PD-L1 inhibitor in combination with anti-angiogenic drug has become the first-line treatment strategy for unresectable hepatocellular carcinoma (HCC). However, PD-L1 inhibitor resistance remains an essential issue in treating HCC. It has been proved in the tumor microenvironment that hypoxia-induced increase in PD-L1 expression makes a significant impact on drug resistance. Methods: Two public datasets of gene expression profiles (GSE 14520 and GSE 41666) from the Gene Expression Omnibus (GEO) database were analyzed using bioinformatics: (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6). HCC-signature and hypoxia-related genes were identified as the differentially expressed genes (DEGs). Gene set enrichment analyses (GSEA) were performed on the DEGs. Furthermore, multiple regression analysis on the TCGA-LIHC dataset (N=371) and construction of the protein-protein interaction (PPI) network were performed to investigate potential PD-L1 regulatory genes and hub genes. Results: A total of 52 genes overlapped in HCC-signature and hypoxia-related DEGs. Five genes were identified by GSEA as being concurrently associated with the PD-L1 expression pathways. Multiple regression analysis results showed 14 potential PD-L1 regulatory genes. Ten hub genes were identified in the PPI network. Finally, three genes (DLGAP5, KIF20A, and TPX2) were found in common and may be regulatory genes affecting PD-L1 expression. Conclusions: In conclusion, our study provides new insights into the potential hypoxia-related mechanisms of PD-L1 inhibitor resistance and contributes to exploring new therapeutic strategies for treating unresectable HCC.

Identification of Key Biomarkers in Hepatocellular Carcinoma Induced by Non-alcoholic steatohepatitis or Metabolic Syndrome via Integrated Bioinformatics Analysis.

The burden of hepatocellular carcinoma (HCC) is steadily growing because obesity, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD) are replacing viral- and alcohol-related liver disease as major pathogenic promoters. The current study attempted to identify the key genes and pathways in the non-alcoholic steatohepatitis (NASH) induced development of HCC using integrated bioinformatics analyses. Two gene expression profiling datasets, GSE102079 and GSE164760 were downloaded. Differentially expressed genes (DEGs) from HCC and healthy control samples were screened. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Expression and survival analysis of hub genes, methylation and genetic mutation analysis were explored withGEPIA2, UALCAN, GSCA, and TIMER2.0 databases. We identified 158 overlapping genes from the 2 datasets. Up-regulated genes were mainly related to the proliferation, adhesion and metastasis of tumors, while down-regulated genes were mainly related to oxidative stress and energy metabolism. CDKN2A, SPP1, CYP2C9 and CYP4A11 were associated with prognostic performance and were considered the potential crucial genes, which SPP1, CYP2C9 and CYP4A11 were identified as the DNA methylation-driven genes. In different mutation statuses of HCC, gene expression of CDKN2A, SPP1, CYP2C9 and CYP4A11 showed significant differences. CDKN2A and SPP1 were identified as risk genes, while CYP2C9 and CYP4A11 were identified as protective genes, which may affect the transformation of NASH into HCC.