Comprehensive Bioinformatics Analysis Research Articles

Deciphering the prognostic significance of anoikis-related lncRNAs in invasive breast cancer: from comprehensive bioinformatics analysis to functional experimental validation.

The global prevalence of breast cancer necessitates the development of innovative prognostic markers and therapeutic strategies. This study investigated the prognostic implications of anoikis-related long non-coding RNAs (ARLs) in invasive breast cancer (IBC), which is an area that has not been extensively explored. By integrating the RNA sequence transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database and employing advanced regression analyses, we devised a novel prognostic model based on ARL scores. ARL scores correlated with diverse clinicopathological parameters, cellular pathways, distinct mutation patterns, and immune responses, thereby affecting both immune cell infiltration and anticipated responses to chemotherapy and immunotherapy. Additionally, the overexpression of a specific lncRNA, AL133467.1, significantly impeded the proliferation and migration, as well as possibly the anoikis resistance of breast cancer cells. These findings highlight the potential of the ARL signature as a robust prognostic tool and a promising basis for personalized IBC treatment strategies.

Comprehensive Analysis Reveals Prognostic and Therapeutic Immunity-Related Biomarkers for Pediatric Metastatic Osteosarcoma.

Background and Objectives: Osteosarcoma, the most prevalent malignant bone tumor in children and adolescents, presents a complex pathogenesis characterized by various genetic and epigenetic alterations. This study aims to identify key differentially expressed genes (DEGs) in pediatric osteosarcoma, with a focus on those influencing metastasis and patient survival. Materials and Methods: We utilized the GSE33382 dataset from the GEO database for a comprehensive bioinformatic analysis. This included a protein-protein interaction (PPI) network analysis, Cox regression, and Kaplan-Meier survival analysis to identify central DEGs associated with osteosarcoma metastasis and patient survival. Results: Our analysis identified 88 DEGs related to osteosarcoma metastasis. Among them, three survival-related central DEGs-C1QA, CD74, and HLA-DMA-were significantly linked to patient outcomes. Further correlation analysis established a strong relationship between these genes, tumor mutation burden (TMB), immune checkpoint gene expression, and overall survival. Notably, C1QA and CD74 exhibited higher expression in non-metastatic osteosarcoma cases, suggesting a potential role in disease progression. Conclusions: The identified DEGs, particularly C1QA, CD74, and HLA-DMA, may serve as critical biomarkers for pediatric osteosarcoma prognosis and potential targets for immunotherapy. These findings provide a deeper understanding of the molecular landscape of osteosarcoma and open new avenues for therapeutic intervention.

Combined transcriptomic and pangenomic analyses guide metabolic amelioration to enhance tiancimycins production.

Exploration of high-yield mechanism is important for further titer improvement of valuable antibiotics, but how to achieve this goal is challenging. Tiancimycins (TNMs) are anthraquinone-fused enediynes with promising drug development potentials, but their prospective applications are limited by low titers. This work aimed to explore the intrinsic high-yield mechanism in previously obtained TNMs high-producing strain Streptomyces sp. CB03234-S for the further titer amelioration of TNMs. First, the typical ribosomal RpsL(K43N) mutation in CB03234-S was validated to be merely responsible for the streptomycin resistance but not the titer improvement of TNMs. Subsequently, the combined transcriptomic, pan-genomic and KEGG analyses revealed that the significant changes in the carbon and amino acid metabolisms could reinforce the metabolic fluxes of key CoA precursors, and thus prompted the overproduction of TNMs in CB03234-S. Moreover, fatty acid metabolism was considered to exert adverse effects on the biosynthesis of TNMs by shunting and reducing the accumulation of CoA precursors. Therefore, different combinations of relevant genes were respectively overexpressed in CB03234-S to strengthen fatty acid degradation. The resulting mutants all showed the enhanced production of TNMs. Among them, the overexpression of fadD, a key gene responsible for the first step of fatty acid degradation, achieved the highest 21.7 ± 1.1mg/L TNMs with a 63.2% titer improvement. Our studies suggested that comprehensive bioinformatic analyses are effective to explore metabolic changes and guide rational metabolic reconstitution for further titer improvement of target products. KEY POINTS: • Comprehensive bioinformatic analyses effectively reveal primary metabolic changes. • Primary metabolic changes cause precursor enrichment to enhance TNMs production. • Strengthening of fatty acid degradation further improves the titer of TNMs.

Identification of hub genes distinguishing subtypes in endometrial stromal sarcoma through comprehensive bioinformatics analysis.

Diagnosing low-grade and high-grade endometrial stromal sarcoma (LG-ESS and HG-ESS) is a challenge. This study aimed to identify biomarkers. 22 ESS cases were analyzed using Illumina microarrays. Differentially expressed genes (DEGs) were identified via Limma. DEGs were analyzed with String and Cytoscape. Core genes were enriched with GO and KEGG, their pan-cancer implications and immune aspects were studied. 413 DEGs were found by exome sequencing, 2174 by GSE85383 microarray. 36 common genes were identified by Venn analysis, and 10 core genes including RBFOX1, PCDH7, FAT1 were selected. Core gene GO enrichment included cell adhesion, T cell proliferation, and KEGG focused on related pathways. Expression was evaluated across 34 cancers, identifying immune DEGs IGF1 and AVPR1A. Identifying the DEGs not only helps improve our understanding of LG-ESS, HG-ESS but also promises to be potential biomarkers for differential diagnosis between LG-ESS and HG-ESS and new therapeutic targets.

Identification of potential diagnostic biomarkers associated with periodontitis by comprehensive bioinformatics analysis

Periodontitis is a chronic inflammatory disease that affects the tissues surrounding the teeth, including the gums and the bones supporting the teeth. Early detection and intervention are crucial for effective management of periodontitis. Our study aims to identify a diagnostic biomarker for periodontitis and explore the pathways associated with the occurrence and development of periodontitis. The expression of gingival tissue from periodontitis and healthy control were downloaded from the Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) were used to analyze module genes associated with periodontitis and DESeq2 were performed to identify differently expressed genes (DEGs) between periodontitis and healthy control. Then the candidate genes were obtained by intersecting the genes from interest modules and DEGs. Functional enrichment analysis was performed using gene ontology and kyoto encyclopedia of gene and genomes, followed by the protein–protein interaction (PPI) network analysis. The hub genes were identified by the cytoCNA plugin in Cytoscape. Finally, immunohistochemical staining of the hub genes was performed to validate the findings. WGCNA analysis found that the expression of the MEblack module was significantly higher in individuals with periodontitis compared to those in the healthy control group. A total of 888 DEGs, including 750 upregulated and 138 downregulated genes, were identified. Finally, 427 candidate genes were identified potentially associated with periodontitis after intersecting the DEGs and the black module genes. Several critical signaling pathways were identified associated with periodontitis by functional enrichment analysis, including cytokine–cytokine receptor interaction, neutrophil extracellular trap formation, Staphylococcus aureus infection, and Interleukin-17 signaling pathway. The PPI network analysis revealed that C-X-C motif chemokine ligand 5 (CXCL5) and C-X-C motif chemokine ligand 6 (CXCL6) could play an important role in the process of periodontitis. The gene expression level of CXCL5 and CXCL6 detected using immunohistochemical verified the findings. In conclusion, we found that CXCL5 and CXCL6 are closely associated with the occurrence of periodontitis. Our present pilot study suggests that CXCL5 and CXCL6 have the potential to be used as a diagnostic biomarker of periodontitis.

Single-cell transcriptomic analysis reveals tumor cell heterogeneity and immune microenvironment features of pituitary neuroendocrine tumors.

Pituitary neuroendocrine tumors (PitNETs) are one of the most common types of intracranial tumors. Currently, the cellular characteristics of normal pituitary and various other types of PitNETs are still not completely understood. We performed single-cell RNA sequencing (scRNA-seq) on 4 normal samples and 24 PitNET samples for comprehensive bioinformatics analysis. Findings regarding the function of PBK in the aggressive tumor cells were validated by siRNA knockdown, overexpression, and transwell experiments. We first constructed a reference cell atlas of the human pituitary. Subsequent scRNA-seq analysis of PitNET samples, representing major tumor subtypes, shed light on the intrinsic cellular heterogeneities of the tumor cells and tumor microenvironment (TME). We found that the expression of hormone-encoding genes defined the major variations of the PIT1-lineage tumor cell transcriptomic heterogeneities. A sub-population of TPIT-lineage tumor cells highly expressing GZMK suggested a novel subtype of corticotroph tumors. In immune cells, we found two clusters of tumor-associated macrophages, which were both highly enriched in PitNETs but with distinct functional characteristics. In PitNETs, the stress response pathway was significantly activated in T cells. While a majority of these tumors are benign, our study unveils a common existence of aggressive tumor cells in the studied samples, which highly express a set of malignant signature genes. The following functional experiments confirmed the oncogenic role of selected up-regulated genes. The over-expression of PBK could promote both tumor cell proliferation and migration, and it was also significantly associated with poor prognosis in PitNET patients. Our data and analysis manifested the basic cell types in the normal pituitary and inherent heterogeneity of PitNETs, identified several features of the tumor immune microenvironments, and found a novel epithelial cell sub-population with aggressive signatures across all the studied cases.

Identification of pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis based on comprehensive bioinformatics analysis and machine learning.

Bioinformatics methods were applied to investigate the pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis (ACAS) and provide new insights for the treatment of this disease. The study utilized five ACAS datasets (GSE100927, GSE11782, GESE28829, GSE41571, and GSE43292) downloaded from the NCBI GEO database. The first four datasets were combined as the training set (n = 99), while GSE43292 (n = 64) was used as the validation set. Difference analysis and functional enrichment analysis were then performed on the training set. The pathogenic targets of ACAS were screened by protein-protein interaction networks and MCODE analyses, combined with three machine learning algorithms. The results were next verified by analysis of inter-group differences and ROC curve analysis. Next, immune-related function and immune cell correlation analyses were performed, and plaques of human ACAS were applied to verify the results via immunohistochemistry (IH) and immunofluorescence (IF). Finally, the competing endogenous RNAs (ceRNA) and transcription factors (TFs) regulatory networks of the characterized genes were constructed. A total of 177 differentially expressed genes were identified, including 67 genes downregulated and 110 genes upregulated. Gene set enrichment analysis revealed that five pathways were active in the experimental group, including xenograft rejection, autoimmune thyroid disease, graft-versus-host disease, leishmaniasis infection, and lysosomes. Four key genes were identified, with C3AR1 being upregulated and FBLN5, PPP1R12A, and TPM1 being downregulated. The analysis of inter-group differences demonstrated that the four characterized genes were differentially expressed in both the control and experimental groups. The ROC analysis showed that they had high AUC values in both the training and validation sets. Therefore, a predictive ACAS patient nomogram model based on the screened genes was established. Correlation analysis revealed a positive correlation between C3AR1 expression and neutrophils, which was further validated in IH and IF. One or multiple lncRNAs may compete with the characterized genes for binding miRNAs. Additionally, each characterized gene interacts with multiple TFs. Four pivotal genes were screened, and relevant ceRNA and TFs were predicted. These molecules may exert a crucial role in ACAS and serve as potential biomarkers and therapeutic targets.

A pancancer analysis of histone deacetylase 3 in human tumors.

Histone deacetylase 3 (HDAC3) is known to be an important role in various kinds of cancer, but its effect has not been examined on the pancancer level. Thus, a systematic pancancer analysis was conducted to explore its potential role in pancancer diagnosis, prognosis, and immune correlation research. We used a series of databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) Project, The University of Alabama at Birmingham Cancer data analysis portal (UALCAN), Tumor Immune Estimation Resource (TIMER), and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), among others, to analyze the relationship between the expression of HDAC3 and the diagnosis and prognosis of cancer, the tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) system using various bioinformatics methods. Downstream pathways of HDAC3 were identified by gene set enrichment analysis (GSEA). Furthermore, the protein expression of HDAC3 in tumor tissues and normal tissues of 17 patients with gliomas was analyzed via western blotting. The expression of HDAC3 changed in most types of tumors, which was closely related to most tumor diagnoses and negatively related to some patients' overall survival (OS) and recurrence-free survival (RFS). The pan-cancer analysis demonstrated that it was tightly correlated to DNA methylation and RNA methylation modifications and associated with TMB and MSI. The expression level of HDAC3 was positively correlated with many immune checkpoint molecules and regulators and positively associated with the infiltration levels of immune cells in the TME in most tumor types. Furthermore, enrichment analysis revealed that transcriptional misregulation in cancer and RNA splicing functions were involved in the functional mechanism of HDAC3-related genes. Experimental research showed that the protein expression of HDAC3 was elevated in tumor tissues of patients with glioma. Through our comprehensive bioinformatics analysis, we evaluated the role of HDAC3 in pancancer, and our findings suggest that it may be an indicator for some cancer diagnoses and influence immune balance.

Glioma-associated oncogene homolog 1 in breast invasive carcinoma: a comprehensive bioinformatic analysis and experimental validation.

Breast cancer, despite significant advancements in treatment, remains a major cause of cancer-related deaths among women. Immunotherapy, an emerging therapeutic strategy, offers promise for better outcomes, particularly through the modulation of immune functions. Glioma-Associated Oncogene Homolog 1 (GLI1), a transcription factor implicated in cancer biology, has shown varying roles in different cancers. However, its immunoregulatory functions in breast invasive carcinoma (BRCA) remain elusive. The current study aimed to unravel the expression patterns and immune-regulatory roles of GLI1 in BRCA. Utilizing multiple bioinformatic platforms (TIMER2.0, GEPIA2, and R packages) based on The Cancer Genome Atlas (TCGA) and/or Genotype-Tissue Expression (GTEx) databases, we analyzed the expression of GLI1 in BRCA and its pan-cancer expression profiles. We further validated these findings by conducting qPCR and immunohistochemical staining on clinical BRCA samples. Kaplan-Meier analysis and Cox proportional hazards regression were performed to assess the prognostic value of GLI1. Additionally, the association between GLI1 expression and immune infiltration within the tumor immune microenvironment (TMIE) was examined. The findings reveal dysregulated expression of GLI1 in numerous cancers, with a significant decrease observed in BRCA. High GLI1 expression indicated better survival outcomes and was correlated with the age and stage of BRCA patients. GLI1 was involved in immune status, as evidenced by its strong correlations with immune and stromal scores and the infiltration levels of multiple immune cells. Meanwhile, GLI1 was co-expressed with multiple immune-related genes, and high GLI1 expression was associated with the activation of immune-related pathways, such as binding to proteasome and mismatch repair and retinol metabolism signaling pathways. Additionally, the differential expression of GLI1 may be related to the effect of immunotherapy on CTLA-4, PD-1, and other signals, and can effectively predict the immune efficacy. Our study underscores the critical role of GLI1 in BRCA, both as a potential tumor suppressor and an immune regulator. The association between GLI1 expression and favorable prognosis suggests its potential as a prognostic biomarker and immunotherapeutic target in BRCA.

Prognostic and immune roles of UROC1 in human cancers: from mechanism exploration of NAFLD and HCC to pan-cancer analysis.

Both non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are prevalent diseases worldwide. This study aimed to explore the underlying mechanisms of NAFLD and HCC and identify new therapeutic targets for human cancers. NAFLD and HCC gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify co-expressed genes associated with NAFLD and HCC. Public databases were consulted to find common targets of NAFLD and HCC. Enrichment analysis and CIBERSORT techniques were employed to analyze the pathways enriched with DEGs and the attributes of infiltrating immune cells. Furthermore, the expression data of UROC1 and clinical information of patients were acquired from The Cancer Genome Atlas (TCGA) database. Finally, the expression of the UROC1 was validated by immunohistochemistry (IHC). Through a comprehensive bioinformatics analysis, eight hub genes (CCL2, CCR2, IL6, CSF3R, ATL2, SESN3, UROC1, FIGNL1) were identified. Enrichment analysis indicated that inflammatory and immune response may be common features between NAFLD and HCC. CIBER-SORT analysis revealed an imbalance of plasma cells and macrophages in NAFLD and HCC. Pan-cancer analysis demonstrated that UROC1 expression was related to clinical outcomes and tumor immunity in various cancers. Moreover, a strong correlation was exhibited between UROC1 expression and crucial elements, including tumor mutation burden (TMB), microsatellite instability (MSI), multiple immune checkpoints (ICP), and tumor microenvironment (TME). Importantly, an adverse clinical prognosis of HCC was linked to decreased UROC1 expression, which was consistent with IHC results. We identified eight hub genes (CCL2, CCR2, IL6, CSF3R, ATL2, SESN3, UROC1, FIGNL1), which may become early diagnostic and therapeutic targets for NAFLD and HCC. The pan-cancer analysis of UROC1 provides new evidence for its broad application prospects in the field of HCC and other cancers.

Bioinformatics-Based Identification of Key Prognostic Genes in Neuroblastoma with a Focus on Immune Cell Infiltration and Diagnostic Potential of VGF.

This study aims to identify differentially expressed genes (DEGs) in neuroblastoma (NB) through comprehensive bioinformatics analysis and machine learning techniques. We seek to elucidate these DEGs' biological functions and associated signaling pathways. Furthermore, our objective extends to predicting upstream microRNAs (miRNAs) and relevant transcription factors of pivotal genes, with the ultimate goal of guiding clinical diagnostics and informing future treatment strategies for Neuroblastoma. In this study, we sourced datasets GSE49710 and TARGET from the GEO and UCSC-XENA databases, respectively. Differentially expressed genes (DEGs) were identified using the R language "limma" package. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were conducted using the "clusterProfiler" package. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to isolate the most significant modules associated with death and MYCN amplification, specifically MEpink and MEbrown modules. These modules were then cross-referenced with the DEGs for further GO and KEGG pathway analyses. LASSO regression analysis, facilitated by the "glmnet" package, was utilized to pinpoint three hub genes. We performed differential analysis on these genes and constructed Receiver Operating Characteristic (ROC) curves for disease diagnosis purposes. Immune infiltration analysis was conducted using the "GSVA" package's ssGSEA function. Additionally, single-gene Gene Set Enrichment Analysis (GSEA) on the hub gene was carried out based on Reactome and KEGG databases. Upstream miRNA and transcription factors associated with the hub gene were predicted using RegNetwork, with visual representations created in Cytoscape. Furthermore, to validate the three identified markers in neuroblastoma tissues, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis was conducted. We identified 483 differentially expressed genes (DEGs) in neuroblastoma. These genes predominantly function in protein translation, membrane composition, and RNA transcription regulation, and are implicated in multiple signaling pathways relevant to neurodegenerative diseases. Utilizing LASSO regression analysis, we pinpointed three hub genes: VGF, DGKD, and C19orf52. The Receiver Operating Characteristic (ROC) curve analysis yielded Area Under Curve (AUC) values of 0.751 and 0.722 for VGF, 0.79 and 0.656 for DGKD, and 0.8 and 0.753 for C19orf52, respectively. Our immune infiltration analysis revealed significant correlations among monocytes, follicular helper T cells, and CD4+ T cells. Notably, in the death group, we observed heightened infiltration levels of activated CD4+ T cells, macrophages, and Th2 cells. C19orf52 exhibited a close association with the infiltration of monocytes, CD4+ T cells, and Th2 cells, with P-values less than 0.05. Furthermore, qRT-PCR analysis corroborated the upregulation of VGF in neuroblastoma tissues, further validating our findings. The hub genes (VGF, DGKD, and C19orf52) of neuroblastoma are screened. VGF, one of the hub genes, may have a high diagnostic value and is involved in the immune cell infiltration in neuroblastoma tissue, which may be used as a biomarker for the diagnosis of neuroblastoma and provides a new direction for clinical prognosis prediction and management improvement.

Identification of hub genes and key pathways in arsenic-treated rice (Oryza sativa L.) based on 9 topological analysis methods of CytoHubba.

Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear. In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in. Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress. In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.

Clinical Significance and Molecular Annotation for PD-L1 Negative Advanced Non-Small Cell Lung Cancer with Sensitivity to Responsive to Dual PD-1/CTLA-4 Blockade.

Immunotherapy has become the standard treatment for driving gene-negative advanced non-small cell lung cancer (NSCLC). However, compared to PD-L1-positive patients, the efficacy of Anti-PD-(L)1 monotherapy is suboptimal in PD-L1-negative advanced NSCLC. In this study, we aim to analyze the optimal immunotherapy approach for PD-L1-negative NSCLC patients and develop a new nomogram to enhance the clinical predictability of immunotherapy for NSCLC patients. In this study, we retrieved clinical information and genomic data from cBioPortal for NSCLC patients undergoing immunotherapy. Cox regression analyses were utilized to screen the clinical information and genomic data that related to survival. The prognostic-relate genes function was studied by comprehensive bioinformatics analyses. The Kaplan-Meier plot method was employed for survival analysis. A total of 199 PD-L1-negative NSCLC patients were included in this study. Among them, 165 patients received Anti-PD-(L)1 monotherapy, while 34 patients received Anti-PD-(L)1+Anti-CTLA-4 combination therapy. The Anti-PD-(L)1+Anti-CTLA-4 combination therapy demonstrated significantly higher PFS compared to the Anti-PD-(L)1 monotherapy. The mutation status of KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 were found to correlate with PFS. Utilizing the clinicopathological parameters and genomic data of the patients, a novel nomogram was developed to predict the prognosis of Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Our study revealed that KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 mutation could serve as predictive biomarkers for patients with Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Our systematic nomogram demonstrates significant potential in predicting the prognosis for NSCLC patients with responsive to dual PD-1/CTLA-4 blockade.

NEK2 promotes TP53 ubiquitination to enhance the proliferation and migration of TP53 wild-type glioblastoma cells.

The most common primary malignant tumor in the adult brain is glioblastoma multiforme (GBM); however, its underlying pathogenic mechanism remains elusive. The never in mitosis (NIMA)-related kinase 2 (NEK2) has been closely associated with the prognosis of various malignancies. Nevertheless, the complete elucidation of NEK2's potential clinical value, particularly in glioma prognosis and development, remains lacking. U87MG and A172 glioblastoma cells were infected with sh-NEK2 lentivirus or oe-NEK2 plasmid to investigate the effect of NEK2 on cell proliferation, migration, and invasion. Cell viability was measured using CCK-8 and colony formation assays, while Transwell assay was utilized to assess cell migration and invasion. Protein expression levels were determined through western blot analysis. Additionally, CGGA and TCGA databases were used for bioinformatics analysis in order to examine the NEK2 expression. Through comprehensive bioinformatics analysis, we identified elevated mRNA expression levels of NEK2 in gliomas compared to normal tissues, which correlated with poor prognosis among glioma patients. Moreover, functional experiments revealed that silencing NEK2 suppressed glioma cell proliferation while overexpression of NEK2 promoted migration and invasion capabilities. Finally, our study uncovered that NEK2 regulates the malignant progression of TP53 wild-type glioblastoma by facilitating TP53 ubiquitination.

GWAS-significant loci and severe COVID-19: analysis of associations, link with thromboinflammation syndrome, gene-gene, and gene-environmental interactions.

The aim of this study was to replicate associations of GWAS-significant loci with severe COVID-19 in the population of Central Russia, to investigate associations of the SNPs with thromboinflammation parameters, to analyze gene-gene and gene-environmental interactions. DNA samples from 798 unrelated Caucasian subjects from Central Russia (199 hospitalized COVID-19 patients and 599 controls with a mild or asymptomatic course of COVID-19) were genotyped using probe-based polymerase chain reaction for 10 GWAS-significant SNPs: rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LLZTFL1, rs17713054 SLC6A20-LLZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2. SNP rs17713054 SLC6A20-LZTFL1 was associated with increased risk of severe COVID-19 in the entire group (risk allele A, OR = 1.78, 95% CI = 1.22-2.6, p = 0.003), obese individuals (OR = 2.31, 95% CI = 1.52-3.5, p = 0.0002, (p bonf = 0.0004)), patients with low fruit and vegetable intake (OR = 1.72, 95% CI = 1.15-2.58, p = 0.01, (p bonf = 0.02)), low physical activity (OR = 1.93, 95% CI = 1.26-2.94, p = 0.0035, (p bonf = 0.007)), and nonsmokers (OR = 1.65, 95% CI = 1.11-2.46, p = 0.02). This SNP correlated with increased BMI (p = 0.006) and worsened thrombodynamic parameters (maximum optical density of the formed clot, D (p = 0.02), delayed appearance of spontaneous clots, Tsp (p = 0.02), clot size 30min after coagulation activation, CS (p = 0.036)). SNP rs17078346 SLC6A20-LZTFL1 was linked with increased BMI (p = 0.01) and severe COVID-19 in obese individuals (risk allele C, OR = 1.72, 95% CI = 1.15-2.58, p = 0.01, (p bonf = 0.02)). SNP rs12610495 DPP9 was associated with increased BMI (p = 0.01), severe COVID-19 in obese patients (risk allele G, OR = 1.48, 95% CI = 1.09-2.01, p = 0.01, (p bonf = 0.02)), and worsened thrombodynamic parameters (time to the start of clot growth, Tlag (p = 0.01)). For rs7949972 ELF5, a protective effect against severe COVID-19 was observed in non-obese patients (effect allele T, OR = 0.67, 95% CI = 0.47-0.95, p = 0.02, (p bonf = 0.04)), improving thrombodynamic parameters (CS (p = 0.02), stationary spatial clot growth rates, Vst (p = 0.02)). Finally, rs12585036 ATP11A exhibited a protective effect against severe COVID-19 in males (protective allele A, OR = 0.51, 95% CI = 0.32-0.83, p = 0.004). SNPs rs67579710 THBS3, THBS3-AS1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs9636867 IFNAR2-were involved in two or more of the most significant G×G interactions (p perm ≤ 0.01). The pairwise combination rs67579710 THBS3, THBS3-AS1 × rs17713054 SLC6A20-LZTFL1 was a priority in determining susceptibility to severe COVID-19 (it was included in four of the top five most significant SNP-SNP interaction models). Overall, this study represents a comprehensive molecular-genetic and bioinformatics analysis of the involvement of GWAS-significant loci in the molecular mechanisms of severe COVID-19, gene-gene and gene-environmental interactions, and provides evidence of their relationship with thromboinflammation parameters in patients hospitalized in intensive care units.

Integrative analysis of autophagy-related genes reveals that CAPNS1 is a novel prognostic biomarker and promotes the malignancy of melanoma via Notch signaling pathway.

Skin cutaneous melanoma (SKCM) is a highly fatal form of skin cancer that develops from the malignant transformation of epidermal melanocytes. There is substantial evidence linking autophagy to cancer etiology and immunotherapy efficacy. This study aimed to conduct a comprehensive analysis of autophagy-related genes (ARGs) using TCGA datasets and further explore the potential function of critical ARGs in SKCM progression. We performed comprehensive bioinformatics analysis uses the TCGA dataset. RT-PCR was applied to examine the expression of CAPNS1 in SKCM cells. Lost-of-function experiments were performed to detect the expression of the related proteins. In this search, we screed 70 differentially expressed autophagy-related genes (DE-ARGs), including 33 up-DE-ARGs and 37 down-DE-ARGs. Enrichment assays revealed that these 70 DE-ARGs may exert influence on critical cellular processes such as autophagy, protein kinase activity, and signaling pathways, impacting cell growth, differentiation, survival, and tumor development. Then, we further explore the prognostic value of 70 DE-ARGs and confirmed 18 survival-related DE-ARGs in SKCM patients. Nearly all the 18 DE-ARGs' methylation was negatively correlated with their corresponding expression in SKCM. The 12 survival-related DE-ARGs were used to develop a unique predictive model that effectively classified SKCM patients into high- and low-risk groups with regard to overall survival. Furthermore, tumor environment analysis indicated that the risk score was associated with several immune cells. Among the 12 survival-related DE-ARGs, our attention focused on CAPNS1 which was highly expressed in SKCM patients and predicted a poor prognosis. In addition, we confirmed that knockdown of CAPNS1 distinctly suppressed the proliferation, metastasis and EMT of SKCM cells, and promoted autophagy via regulating Notch signaling pathway. Overall, this study enhances our understanding of the intricate molecular landscape of SKCM progression and presents promising avenues for future research and clinical applications.

The upregulation of TGM2 is associated with poor prognosis and the shaping of the inflammatory tumor microenvironment in lung squamous cell carcinoma.

Tissue transglutaminase (TGM2) is a member of the glutamine transferase superfamily, located within cells and their membranes. When secreted, it catalyzes the cross-linking of extracellular matrix proteins and promotes the formation of extracellular matrix scaffolds. To determine the function of TGM2 in the tumorigenesis of lung squamous cell carcinoma (LUSC), we conducted a comprehensive bioinformatics analysis of TGM2. Our findings indicate that high expression of TGM2 in LUSC was associated with a poorer prognosis. Additionally, we found that high expression of TGM2 is closely related to tumor-promoting inflammation and may increase sensitivity to immunotherapy. We further confirmed the cancer-promoting effect of TGM2 in LUSC through in vitro overexpression and knockdown experiments and showed that TGM2 primarily affects cancer cell proliferation, apoptosis, and invasion. In summary, TGM2 promoted the progression of LUSC, and targeting TGM2 is expected to become a new therapeutic approach for LUSC treatment.

Analyzing secretory proteins in human dermal fibroblast-conditioned medium for angiogenesis: A bioinformatic approach.

The conditioned medium from human dermal fibroblasts (dermal fibroblast-conditioned medium; DFCM) contains a diverse array of secretory proteins, including growth factors and wound repair-promoting proteins. Angiogenesis, a crucial process that facilitates the infiltration of inflammatory cells during wound repair, is induced by a hypoxic environment and inflammatory cytokines. In this study, we conducted a comprehensive bioinformatic analysis of 337 proteins identified through proteomics analysis of DFCM. We specifically focused on 64 DFCM proteins with potential involvement in angiogenesis. These proteins were further classified based on their characteristics, and we conducted a detailed analysis of their protein-protein interactions. Gene Ontology protein classification categorized these 64 DFCM proteins into various classes, including metabolite interconversion enzymes (N=11), protein modifying enzymes (N=10), protein-binding activity modulators (N=9), cell adhesion molecules (N=6), extracellular matrix proteins (N=6), transfer/carrier proteins (N=3), calcium-binding proteins (N=2), chaperones (N=2), cytoskeletal proteins (N=2), RNA metabolism proteins (N=1), intercellular signal molecules (N=1), transporters (N=1), scaffold/adaptor proteins (N=1), and unclassified proteins (N=9). Furthermore, our protein-protein interaction network analysis of DFCM proteins revealed two distinct networks: one with medium confidence level interaction scores, consisting of 60 proteins with significant connections, and another at a high confidence level, comprising 52 proteins with significant interactions. Our bioinformatic analysis highlights the presence of a multitude of secretory proteins in DFCM that form significant protein-protein interaction networks crucial for regulating angiogenesis. These findings underscore the critical roles played by DFCM proteins in various stages of angiogenesis during the wound repair process.

Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis.

Diabetic nephropathy (DN) is a severe complication of diabetes influenced by arginine methylation. This study aimed to elucidate the role of protein arginine methylation-related genes (PRMT-RGs) in DN and identify potential biomarkers. Differentially expressed genes in two GEO datasets (GSE30122 and GSE104954) were integrated with 9 PRMT-RGs. Candidate genes were identified using WGCNA and differential expression analysis, then screened using support vector machine-recursive feature elimination and least absolute shrinkage and selection operator. Biomarkers were defined as genes with consistent differential expression across both datasets. Regulatory networks were constructed using the miRNet and Network Analyst databases. Gene set enrichment analysis was performed to identify the signaling pathways in which the biomarkers were enriched in DN. Different immune cells in DN were identified using immune infiltration analysis. Meanwhile, drug prediction and molecular docking identified potential DN therapies. Finally, qRT-PCR and immunohistochemistry validated two biomarkers in STZ-induced DN mice and DN patients. Two biomarkers (FAM98A and FAM13B) of DN were identified in this study. The molecular regulatory network revealed that FAM98A and FAM13B were co-regulated by 6 microRNAs and 1 transcription factor and were enriched in signaling pathways. Immune infiltration and correlation analyses revealed that FAM98A and FAM13B were involved in developing DN along with PRMT-RGs and immune cells. The expression levels of Fam98a and Fam13b were significantly upregulated in the kidneys of DN mice revealed by qRT-PCR analysis. The expression levels of FAM98A were significantly upregulated in the kidneys of DN patients revealed by immunohistochemistry staining. Molecular docking showed that estradiol and rotenone exerted potential therapeutic effects on DN by targeting FAM98A. Comprehensive bioinformatics analysis revealed that FAM98A and FAM13B were potential DN biomarkers correlated with PRMT-RGs and immune cells. This study provided useful insights for elucidating the molecular mechanisms and developing targeted therapy for DN.

Discovery of ferroptosis-related genes in renal ischemia reperfusion and evaluate the potential impact on kidney transplantation.

Renal ischemia reperfusion injury (IRI) is one of the pivotal event of acute kidney injury (AKI), and they are unavoidable in the process of kidney transplantation, which eventually leads to the loss of renal allograft. Ferroptosis is a newly identified programmed cell death. Recent studies have suggested that ferroptosis may participate in the pathophysiological process of renal IRI. Therefore, we aimed to determine biomarkers associated with ferroptosis during renal IRI and their impact on renal allografts. We conducted a comprehensive bioinformatics analysis and established an IRI-AKI animal model to illustrate the critical role of ferroptosis-related hub genes (FRHGs) in IRI-AKI and their potential impact on kidney transplantation. In this study, we identified 60 ferroptosis-related genes (FRGs) in renal IRI based on the GSE148420 dataset and FerrDb database. And then we performed functional annotation analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein-protein interaction (PPI) network was constructed by online tool String. EZH2, CDKN1A, PPARA, EGR1, ATF3, and CD44 were identited as six ferroptosis-related hubgenes (FRHGs) using four methods, including MMC, Degree, DMNC, and EPC. FRHGs expression level were verified by the validation sets GSE58438 and GSE126805. Protein expression level of FRHGs verified by Proteomics and Western blot. Cibersort was utilized to analyze immune cell infiltration during renal IRI as well as the correlation between FRHGs and immune cells. The GSE21374 dataset was used for renal allografts survival analysis. Finally, We induced the IRI-AKI animal model and illustrated the importance of FRGHs CD44 in ferroptosis and the accumulation of macrophages. We identified 6 FRHGs. We found that FRHGs not only exhibited significant correlation with immune cells but also directly influenced the survival of transplanted kidneys in the human population. Among six FRHGs, only CD44 was overexpressed at both the gene and protein levels. Anti-CD44 exerts a protective effect by inhibiting ferroptosis and the accumulation of M1 macrophages during renal IRI. This study provided new insights into the pathogenesis of renal IRI and provided new evidence for its treatment.