Introducing Aurora Kinase A as a Gene Target Against Photoaging: A Network Analysis

BackgroundPulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.MethodsSingle-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.ResultsNineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.ConclusionWDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

To study disease development, an inventory of an organ's cell types and understanding of physiologic function is paramount. Here, we performed single-cell RNA-sequencing to examine heterogeneity of murine pancreatic duct cells, pancreatobiliary cells, and intrapancreatic bile duct cells. We describe an epithelial-mesenchymal transitory axis in our three pancreatic duct subpopulations and identify osteopontin as a regulator of this fate decision as well as human duct cell dedifferentiation. Our results further identify functional heterogeneity within pancreatic duct subpopulations by elucidating a role for geminin in accumulation of DNA damage in the setting of chronic pancreatitis. Our findings implicate diverse functional roles for subpopulations of pancreatic duct cells in maintenance of duct cell identity and disease progression and establish a comprehensive road map of murine pancreatic duct cell, pancreatobiliary cell, and intrapancreatic bile duct cell homeostasis.

Introduction: Time-dependent effects of laser radiation have been investigated by researchers. An understanding of the molecular mechanism of the time course effect of the laser needs molecular assessment and function evaluation of the related genes. In the present study, the importance of repetition of treatment after 4 weeks and gene expression alteration after 7 days of laser radiation versus one day on the human skin was evaluated via protein-protein interaction (PPI) network analysis and gene ontology enrichment. Methods: The differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus (GEO) and assessed via PPI network analysis. The critical DEGs were enriched via gene ontology. The related biological processes and biochemical pathways were retrieved from "GO-Biological process" and "Kyoto Encyclopedia of Genes and Genomes" (KEGG) respectively. Results: The repetition of laser therapy after 4 weeks of the first treatment did not have a significant effect on treatment efficacy. Sixty-three significant DEGs and six classes of biological terms discriminated the samples seven days after the treatment from individuals one day after the treatment. The studied DEGs were organized into two clusters with certain functions. Conclusion: Based on the findings after laser therapy, several days are required to complete the critical processes such as DNA biosynthesis and skin cornification.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

Decision letter: Single-cell transcriptomic atlas of lung microvascular regeneration after targeted endothelial cell ablation

Author response: Tau polarizes an aging transcriptional signature to excitatory neurons and glia

BackgroundLung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.MethodsThe scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.Results424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.ConclusionSeven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

Identification of the etiology, molecular mechanisms, and carcinogenic pathways of tongue squamous cell carcinoma (TSCC) is crucial for developing new diagnostic and therapeutic strategies. This study used bioinformatics methods to identify key genes in TSCC and explored the potential functions and pathway mechanisms related to the malignant biological behavior of TSCC. Gene chip data sets (i.e., GSE13601 and GSE34106) containing the data of both TSCC patients and normal control subjects were selected from the Gene Expression Omnibus (GEO) database. Using a gene expression analysis tool (GEO2R) of the GEO database, the differentially expressed genes (DEGs) were identified using the following criteria: |log fold change| >1, and P<0.05. The GEO2R tool was also used to select the upregulated DEGs in the chip candidates based on a P value <0.05. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) function analysis, and a protein-protein interaction (PPI) network analysis were then conducted. The results were displayed using R language packages, including volcano plots, Venn diagrams, heatmaps, and enriched pathway bubble charts. Genes from the MalaCards database were compared with the candidate genes, and a thorough review of the literature was conducted to determine the clinical significance of these genes. Finally, feature gene-directed chemical drugs or targeted drugs were predicted using the Comparative Toxicogenomics Database (CTD). In total, 767 upregulated DEGs were identified from GSE13601 and 695 from GSE34106. By intersecting the upregulated DEGs from both data sets using a Venn diagram, 100 DEGs related to TSCC were identified. The enrichment analysis of the KEGG signaling pathways identified the majority of the pathways associated with the upregulated DEGs, including the Toll-like receptor signaling pathway, the extracellular matrix-receptor interaction, the tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, the chemokine signaling pathway, the interlukin-17 signaling pathway, and natural killer cell-mediated cytotoxicity. The PPI network and module analyses of the shared DEGs ultimately resulted in five clusters and 55 candidate genes. A further intersection analysis of the TSCC-related genes in the MalaCards database via a Venn diagram identified three important shared DEGs; that is, matrix metalloproteinase-1 (MMP1), MMP9, and MMP13. In the CTD, seven drugs related to MMP13 were identified for treating tongue tumors. This study identified key genes and signaling pathways involved in TSCC and thus extended understandings of the molecular mechanisms that underlie the development and progression of TSCC. Additionally, this study showed that MMP13 may influence the malignant biological behavior of TSCC through the TNF signaling pathway. This finding could provide a theoretical basis for research into early differential diagnosis and targeted treatment.

Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data.Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated.Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them.Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.

BackgroundThis study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq).MethodsBulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups.ResultsThe analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher’s exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276.ConclusionWe built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work.

BackgroundOvarian cancer (OC) has the highest mortality rate among all gynecological cancers, yet its pathogenesis remains unclear. This study aims to use integrated bioinformatics methods to identify important biomarkers and subtypes closely related to tumor immunity and fatty acid synthesis in OC.MethodsRNA sequencing data offered with the Gene Expression Omnibus (GEO) were processed. Differentially expressed genes (DEGs) were screened and annotated via Gene Ontology (GO) Enrichment Analysis, Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis (GSEA). Besides, the critical DEGs were used in building the protein-protein interaction (PPI) networks, screening significant subtypes, and constructing risk models.ResultsAfter processing the raw data derived from GEO, we filtered out 1,401 DEGs, which were used in gene enrichment and building the PPI networks. Several processes were enriched. Three subtypes associated with fatty acid synthesis and tumor immunity in OC were identified based on six critical genes (RYBP, RNF2, RGL2, RCOR3, SMURF2, and SESN3). Additionally, we constructed the PPI networks and defined different immune or lipid metabolic subtypes based on the DEGs. Finally, we established the model to predict risk in OC patients via the least absolute shrinkage and selection operator (LASSO) regression. Model validation was performed using The Cancer Genome Atlas (TCGA) OC expression profiles as an independent dataset.ConclusionsThis study enhances our understanding of the complex molecular mechanisms underlying OC by highlighting the interplay between tumor immunity and fatty acid synthesis. The identification of three distinct subtypes based on key genes provides a new framework for categorizing OC patients, which could lead to more personalized therapeutic approaches. The prognostic model related to fatty acid synthesis not only offers potential biomarkers for predicting patient outcomes but also suggests new avenues for targeted therapies. These findings could pave the way for more effective immune-based treatments and improve the prognosis for OC patients. Future research should focus on validating these biomarkers and exploring their functional roles in OC pathogenesis and treatment response.

BackgroundThe prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA.MethodsBLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated.ResultsscRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy.ConclusionBy integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

PurposeIschemic cardiomyopathy (ICM) is considered to be the most common cause of heart failure, with high prevalence and mortality. This study aimed to investigate the different expressed genes (DEGs) and pathways in the pathogenesis of ICM using bioinformatics analysis.MethodsThe control and ICM datasets GSE116250, GSE46224 and GSE5406 were collected from the gene expression omnibus (GEO) database. DEGs were identified using limma package of R software, and co-expressed genes were identified using Venn diagrams. Then, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore the biological functions and signaling pathways. Protein–protein interaction (PPI) networks were assembled with Cytoscape software to identify hub genes related to the pathogenesis of ICM. RT-PCR of Heart tissues (n=2 for non-failing controls and n=4 for ischemic cardiomyopathy patients) was used to validate the bioinformatic results.ResultsA total of 844 DEGs were screened from GSE116250, of which 447 were up-regulated genes and 397 were down-regulated genes, respectively. A total of 99 DEGs were singled out from GSE46224, of which 58 were up-regulated genes and 41 were down-regulated genes, respectively. Thirty DEGs were screened from GSE5406, including 10 genes with up-regulated expression and 20 genes with down-regulated expression. Five up-regulated and 3 down-regulated co-expressed DEGs were intersected in three datasets. GO and KEGG pathway analyses revealed that DEGs are mainly enriched in collagen fibril organization, protein digestion and absorption, AGE-RAGE signaling pathway and other related pathways. Collagen alpha-1(III) chain (COL3A1), collagen alpha-2(I) chain (COL1A2) and lumican (LUM) are the three hub genes in all three datasets through PPI network analysis. The expression of 5 DEGs (SERPINA3, FCN3, COL3A1, HBB, MXRA5) in heart tissues by qRT-PCR results was consistent with our GEO analysis, while expression of 3 DEGs (ASPN, LUM, COL1A2) was opposite with GEO analysis.ConclusionThese findings from this bioinformatics network analysis investigated key hub genes, which contributed to better understanding the mechanism and new therapeutic targets of ICM.

BackgroundPatients with heart failure (HF) have a poor prognosis and continue to pose a global threat to human health. Consequently, it is crucial to employ bioinformatic approaches to analyze functional alterations within the transcriptome. This analysis should be conducted in conjunction with transcriptome sequencing data from a large sample of clinical myocardial tissue, in order to identify the core pathogenic mechanisms in heart failure myocardial tissue.MethodTranscriptome data from HF patient myocardial biopsies underwent Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). These DEGs were intersected with key genes identified via Weighted Gene Co-expression Network Analysis (WGCNA) in HF. Functional enrichment analysis was performed on the DEGs. Selected key genes were experimentally validated using RT-qPCR in hypertrophic cardiomyocyte models. Single-cell data dimensionality reduction, clustering, and visualization were achieved using Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). Cell types were annotated with SingleR and CellMarker, and single-cell functional enrichment was performed using the “irGSEA” R package.ResultsRRA of transcriptome data from five studies identified 102 DEGs. Functional enrichment analyses (GO, KEGG, GSEA) revealed associated functional alterations. WGCNA highlighted a key module enriched for energy metabolism-related genes, with the mitochondrial matrix and inner membrane identified as their primary subcellular locations. Integrating RRA-derived DEGs with WGCNA key module genes yielded 14 crucial genes, validated experimentally in a hypertrophic cardiomyocyte model. Analysis of single-cell RNA-seq data identified cold shock domain containing C2 (CSDC2) and Single-pass membrane and coiled-coil domain-containing protein 4 (SMCO4) as cardiomyocyte-specific genes within this set. Subpopulations of cardiomyocytes with high or low expression of SMCO4 and CSDC2 showed strong associations with alterations in fatty acid metabolism, adipogenesis, and oxidative phosphorylation pathways.ConclusionIntegrated transcriptomic analysis identified 12 key genes linked to HF, which were validated in a hypertrophy model. Single-cell data showed SMCO4 and CSDC2 are specifically expressed in cardiomyocytes and regulate fatty acid metabolism. This suggests SMCO4 and CSDC2 contribute to HF by altering fatty acid metabolism in heart cells, revealing new disease mechanisms.

Introduction: Photodynamic therapy (PDT) is a photochemical treatment that involves the use of light and photosensitizer. This method is applied as a therapeutic approach against several types of cancer. The main aim of this study is to compare the efficacy of PDT with that of cisplatin (a well-known chemotherapy agent) through protein-protein interaction (PPI) network analysis. Methods: Gene expression profiles of human melanoma A375 cells from the Gene Expression Omnibus (GEO) were selected for analysis via directed PPI network analysis. The significant differentially expressed genes (DEGs) were identified and assessed based on co-expression interactions. The critical DEGs were introduced by considering out-degree and in-degree values. Results: Two directed PPI networks for upregulated and downregulated DEGs were constructed. TP53 was identified as a critical upregulated gene in response to cisplatin in comparison with PDT. EGFR, PPARG, MMP9, PTGS2, FOXO1, and RUNX2 were highlighted as the crucial downregulated genes due to the effect of cisplatin on the gene expression of the treated cells. Conclusion: Cisplatin directly targets key cellular functions such as cell growth, differentiation, migration, and invasion. It seems that the combination of cisplatin and PDT is a suitable method for treating cancers because cisplatin targets the key genes responsible for cancer development, while PDT intensifies the effect of cisplatin and reduces its side effects.