Detect Differentially Expressed Genes Research Articles

Abstract PO4-23-11: Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing

Abstract Background Understanding the mechanisms of resistance to CDK4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is pivotal in exploring new therapeutic strategies for hormone receptor-positive (HR+), HER2-negative metastatic breast cancer. To decipher these resistance mechanisms, we analyzed the alterations in comprehensive RNA-seq (coding and non-coding RNAs) of HR+ HER2-negative BC cell lines via thermostable group II intron reverse transcriptase sequencing (TGIRT-seq). Methods We established Tamoxifen-resistant (TMR), Abemaciclib-resistant (ACR), Palbociclib-resistant (PCR), Tamoxifen/Abemaciclib double-resistant (TMR-ACR), and Tamoxifen/Palbociclib double-resistant (TMR-PCR) BC cell lines from MCF7 and T47D HR+/HER2- BC cell lines through stepwise dose-escalation continuous drug exposure. We performed a TGIRT-seq transcriptomic analysis using a protocol that allows the sequencing of both long and short non-coding and protein-coding RNAs in a single library. All libraries were sequenced using paired-end 150 bp on the Novaseq platform, resulting in an average of 50 million reads per library. For analysis, raw reads underwent adapter trimming, small RNA mapping, whole genome mapping, and the generation of annotated genes' read count. We used raw counts to detect differentially expressed genes (DEGs) with DESeq2 in R using the cut-off (log2 [fold change] > 1, FDR < 1e-3). We then used the selected DEGs for Gene Set Enrichment Analysis (GSEA) and Kaplan-Meier survival analysis from the TCGA database. Results The TGIRT-seq analysis identified 1171 to 3472 DEGs in different drug resistance and cell line combinations. Most DEGs in resistant cells were consistently downregulated compared to parent cells. Seventy-five percent of DEGs were protein-coding genes, with the rest being non-coding RNAs, such as small nucleolar RNAs, microRNAs, long non-coding RNAs, and transposable element RNAs, which conventional RNA-seq poorly detects. In principal component analysis (PCA) plots, replicates per drug resistance clustered clearly in both cell line backgrounds. PCA also indicated distinctive pathways for acquiring drug resistance in MCF7 and T47D cells. The estrogen receptor (ER) expression decreased, while HER2 increased in resistant cell lines. DEGs identified were enriched in ESR1-related pathways in all MCF7 and T47D cell lines resistant models. Several ER-regulating genes like IL-1R1 and RET were upregulated, while ADCY1 was consistently downregulated across different resistance types. A significant overlap with single-resistance DEGs was observed among the DEGs in double-resistance cell lines, but 20-50% of up/down-regulated DEGs in double-resistance cell lines were uniquely altered. Several previously identified DEGs (e.g., SALL4, TOP2A) and 21 novel candidate genes correlated with poor survival outcomes. Conclusion The analysis identified unique DEGs in double resistance cell lines, suggesting that double resistance mechanisms may not merely be a cumulative effect of single resistance mechanisms, necessitating further investigation and validation. CDK4/6i and/or ET-resistant BC cell lines displayed significant transcriptome reprogramming during the development of drug resistance. In previously published research, ESR1-related pathway alterations were proposed in tamoxifen resistance cell lines. Here we find that CDK4/6i-resistant cells also modify these ESR1-related pathways. Additionally, we identified several targetable genes, such as IL-1R1 and RET, involved in ESR1-related pathways that could pave the way for developing new treatment strategies. Citation Format: Toshiaki Iwase, Hengyi Xu, Nakyung Oh, Jangsoon Lee, Alan M Lambowitz, Naoto Ueno. Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing [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 PO4-23-11.

Bioinformatics analysis characterizes immune infiltration landscape and identifies potential blood biomarkers for heart transplantation

Background: Cardiac allograft rejection (AR) remains a significant complication following heart transplantation. The primary objective of our study is to gain a comprehensive understanding of the fundamental mechanisms involved in AR and identify possible therapeutic targets.Methods: We acquired the GSE87301 dataset from the Gene Expression Omnibus database. In GSE87301, a comparison was conducted on blood samples from patients with and without cardiac allograft rejection (AR and NAR) to detect differentially expressed genes (DEGs). Enrichment analysis was conducted to identify the pathways that show significant enrichment during AR. Machine learning techniques, including the least absolute shrinkage and selection operator regression (LASSO) and random forest (RF) algorithms, were employed to identify potential genes for the diagnosis of AR. The diagnostic value was evaluated using a nomogram and receiver operating characteristic (ROC) curve. Additionally, immune cell infiltration was analyzed to explore any dysregulation of immune cells in AR.Results: A total of 114 DEGs were identified from the GSE87301 dataset. These DEGs were mainly found to be enriched in pathways related to the immune system. To identify the signature genes, the LASSO and RF algorithms were used, and four genes, namely ALAS2, HBD, EPB42, and FECH, were identified. The performance of these signature genes was evaluated using the receiver operating characteristic curve (ROC) analysis, which showed that the area under the curve (AUC) values for ALAS2, HBD, EPB42, and FECH were 0.906, 0.881, 0.900, and 0.856, respectively. These findings were further confirmed in the independent datasets and clinical samples. The selection of these specific genes was made to construct a nomogram, which demonstrated excellent diagnostic ability. Additionally, the results of the single-sample gene set enrichment analysis (ssGSEA) revealed that these genes may be involved in immune cell infiltration.Conclusion: We identified four signature genes (ALAS2, HBD, EPB42, and FECH) as potential peripheral blood diagnostic candidates for AR diagnosis. Additionally, a nomogram was constructed to aid in the diagnosis of heart transplantation. This study offers valuable insights into the identification of candidate genes for heart transplantation using peripheral blood samples.

Comparative transcriptome profiling reveals differential defense responses among Alternaria brassicicola resistant Sinapis alba and susceptible Brassica rapa.

Alternaria blight is a devastating disease that causes significant crop losses in oilseed Brassicas every year. Adoption of conventional breeding to generate disease-resistant varieties has so far been unsuccessful due to the lack of suitable resistant source germplasms of cultivated Brassica spp. A thorough understanding of the molecular basis of resistance, as well as the identification of defense-related genes involved in resistance responses in closely related wild germplasms, would substantially aid in disease management. In the current study, a comparative transcriptome profiling was performed using Illumina based RNA-seq to detect differentially expressed genes (DEGs) specifically modulated in response to Alternaria brassicicola infection in resistant Sinapis alba, a close relative of Brassicas, and the highly susceptible Brassica rapa. The analysis revealed that, at 48 hpi (hours post inoculation), 3396 genes were upregulated and 23239 were downregulated, whereas at 72 hpi, 4023 genes were upregulated and 21116 were downregulated. Furthermore, a large number of defense response genes were detected to be specifically regulated as a result of Alternaria infection. The transcriptome data was validated using qPCR-based expression profiling for selected defense-related DEGs, that revealed significantly higher fold change in gene expression in S. alba when compared to B. rapa. Expression of most of the selected genes was elevated across all the time points under study with significantly higher expression towards the later time point of 72 hpi in the resistant germplasm. S. alba activates a stronger defense response reaction against the disease by deploying an array of genes and transcription factors involved in a wide range of biological processes such as pathogen recognition, signal transduction, cell wall modification, antioxidation, transcription regulation, etc. Overall, the study provides new insights on resistance of S. alba against A. brassicicola, which will aid in devising strategies for breeding resistant varieties of oilseed Brassica.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AimGastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.MethodsGSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.ResultsIn total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was “Protein digestion and absorption.” The most significant identified GO terms included “primary alcohol metabolic process,” “basal part of cell,” and “extracellular matrix structural constituent conferring tensile strength.” Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.ConclusionsThe present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

MUM1L1 as a Tumor Suppressor and Potential Biomarker in Ovarian Cancer: Evidence from Bioinformatics Analysis and Basic Experiments.

Ovarian cancer (OC) is the most prevalent gynecologic malignancy, with high mortality rates. However, its pathogenesis remains unclear. The current study aimed to explore potential biomarkers and suppressor genes for diagnosing and treating OC. Biochemical and bioinformatics approaches were used to detect differentially expressed genes (DEGs) in ovarian tissues via integration analysis. Kaplan-Meier plot analysis was performed to assess progression-free survival and overall survival according to DEGs. Then, we constructed a protein-protein interaction (PPI) network based on data from the STRING database to identify the related target genes of DEGs. Finally, DEGs regulating the proliferation, migration, and invasion of SKOV3 cell lines were validated via in vitro experiments. Four DEGs (MUM1L1, KLHDC8A, CRYGD, and GREB1) with enriched expression in ovarian tissues were explicitly expressed in the ovary based on an analysis of all human proteins. MUM1L1 had high specificity, and its expression was higher in normal ovarian tissues than in OC tissues. Kaplan-Meier plot analysis showed that a high MUM1L1 expression was associated with longer progression-free survival and overall survival in OC. Based on the PPI analysis results, CBLN4, CBLN1, PTH2R, TMEM255B, and COL23A1 were associated with MUM1L1. In vitro studies revealed that MUM1L1 overexpression decreased the proliferation, migration, and invasion ability of SKOV3 cell lines. Meanwhile, MUM1L1 knockdown had contrasting results. MUM1L1 is a tumor suppressor gene and is a potential biomarker for diagnosing and treating OC.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis

BackgroundThoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear.MethodsThree Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues.ResultsTwenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses.ConclusionSLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses.

Differential Gene Expression Profiling in Alveolar Echinococcosis Identifies Potential Biomarkers Associated With Angiogenesis.

Alveolar echinococcosis (AE) is a worldwide zoonosis caused by Echinococcus multilocularis. Alveolar echinococcosis is a severe chronic parasitic disease that exhibits a tumor-like growth, with the potential for invasion and distant metastasis; however, the molecular mechanism underlying this condition remains unclear. Transcriptome analyses were performed to detect differentially expressed genes (DEGs) in samples from patients with AE with invasion and distant metastasis. The results were further verified by immunohistochemistry. A total of 1796 DEGs were identified, including 1742 upregulated and 54 downregulated DEGs. A subsequent functional analysis showed that the significant DEGs were involved in the angiogenesis process. Immunohistochemical analysis confirmed the reliability of the transcriptomic data. These results suggest that angiogenesis is a possible mechanism underlying the tumor-like biological behavior observed during E multilocularis infection. Genes related to this process may play important roles in AE invasion and distant metastasis.

RNA sequencing analysis reveals the potential therapeutic mechanisms of Huzhang Tongfeng granule in MSU-induced acute gouty arthritis mouse model

BackgroundGout is one of the most prevailing inflammatory arthritis, in which joint pain and dysfunction may drastically reduce patients' quality of life, work efficiency, and increase the burden of medical treatment. The Huzhang Tongfeng granule (HZTFG) has been utilized clinically for more than 30 years with satisfactory effects and no significant adverse reactions. However, the mechanisms that underlie HZTFG's effect remain indeterminate. MethodsFirstly, the Liquid Chromatography-Mass Spectrometry (LC-MS) analyses were applied to clarify the multi-components of HZTFG to establish the quality control parameters. After the monosodium urate (MSU) crystals induced Acute gouty arthritis (AGA) model was established, behavioral testing, joint swelling, hematoxylin, and eosin (H&E) staining were applied to assess the effect of HZTFG on improving joint pain, swelling, synovial inflammation of the MSU crystals-injected mice. Moreover, to further understand the underlying mechanism of the action of HZTFG on AGA, RNA Sequencing (RNA-seq) analysis was performed to detect differentially expressed genes (DEGs) after HZTFG treatment. Thereafter, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched via gene set enrichment analysis (GSEA) to assess the functions and pathways of DEGs. Furthermore, Reverse Transcription Polymerase Chain Reaction (RT-qPCR) analysis and enzyme-linked immunosorbent assay (ELISA) were applied for validation in vivo. ResultsAfter treatment with HZTFG, RNA-seq results revealed 44 DEGs, of which 20 genes were remarkably down-regulated and 24 genes were up-regulated. Besides, the expression of these assayed genes were noticeably decreased, including Cxcl1, Cxcl2, Cebpb, Lcn2, Ptgs2, Mmp13, Fos, Socs3, Sele, and Junb. Moreover, animal experiments also demonstrated that TNF signaling pathway and IL-17 signaling pathway are key pathways in HZTFG treated MSU-induced AGA mouse model. ConclusionHZTFG attenuated MSU crystals-induced inflammatory response through TNF and IL-17 signaling pathways by inhibiting the activation of related chemokines, inflammatory proteins, transcription factors, suppressor of cytokine signaling, and adhesion molecule in AGA. This study further provides the potential therapeutic targets for AGA treatment.

Transcriptomic analysis identifies shared biological foundations between ischemic stroke and Alzheimer's disease.

Alzheimer's disease (AD) and ischemic stroke (IS), two major neurological diseases, are suggested to be associated in clinical and pathophysiological levels. Previous studies have provided some insights into the possible genetic mechanisms behind the correlation between AD and IS, but this issue is still not clear. We implemented transcriptomic analysis to detect common hub genes and pathways to help promote the understanding of this issue. Four gene expression profiling datasets (GSE16561, GSE58294, GSE63060, and GSE63061) of peripheral whole blood, which contain 108 IS samples, 284 AD samples, and 285 matched controls, were employed to detect differentially expressed genes (DEGs) for AD and IS, which were further analyzed for shared biological pathways, candidate drugs, and transcription factors. Protein-protein interaction (PPI) network and drug-target interaction analysis were applied to identify hub genes and drug targets, respectively. Result verification was done with other independent datasets (GSE37587, GSE46480, and GSE140829). The difference in proportions of various immune cells in the peripheral blood of AD and IS patients were evaluated using CIBERSORT. We identified 74 DEGs and 18 biological processes with statistical significance shared by AD and IS, 9 of which were immune-related pathways. Five hub genes scored high in the topological analysis of the PPI network, and we also found eight drug target genes and candidate drugs which were associated with AD and IS. As for immunological changes, an increase in the proportion of M0 macrophages was found in the peripheral circulation of both AD and IS patients, and SOD1 expression was significantly correlated with this change. Collectively, the common DEGs and shared pathways found in this study suggest a potential shared etiology between AD and IS, behind which immune system, particularly the M0 macrophage elevation, might have important roles. While, the shared hub genes, potential therapeutic gene targets and drugs reported in this study provide promising treatment strategies for AD and IS.

YTHDC1 Promotes Stemness Maintenance and Malignant Progression in Head and Neck Squamous Cell Carcinoma.

YTH domain containing 1 (YTHDC1), an N6-methyladenosine (m6A) modification reading protein, plays a key role in regulating RNA translation and degradation. However, the role of YTHDC1 in head and neck squamous cell carcinoma (HNSCC) cancer stem cells remains largely unknown. This study is aimed at investigating the role of YTHDC1 in HNSCC and exploring its role in regulating cancer stem cells. RNA sequencing was used to detect differentially expressed genes (DEGs) between SCC9 spheres and SCC9 cells and to uncover molecular pathways and target molecules associated with CSCs. We detected YTHDC1 expression in The Cancer Genome Atlas (TCGA) database data and clinical samples. Subsequently, YTHDC1 gene suppression assays were performed in HNSCC cell lines to investigate the effect of YTHDC1 on tumor cell stemness maintenance, proliferation, and migration capacity. To further confirm the role of YTHDC1 in regulating cancer stem cells in HNSCC, we analyzed online HNSCC single-cell transcriptomic data to investigate YTHDC1 expression patterns at the single-cell level and the correlation of these levels with the expression of stem cell markers. YTHDC1 expression levels were significantly upregulated in SCC9 spheres, and YTHDC1 was aberrantly expressed in HNSCC tumor tissues. The increased YTHDC1 expression was closely correlated with the clinical characteristics of HNSCC patients. YTHDC1 regulates the malignant phenotype of HNSCC in both in vivo and in vitro studies. Further single-cell transcriptomic data analysis revealed that YTHDC1 positively correlated with malignant epithelial cell stemness capacity at the single-cell level, and that YTHDC1 was involved in regulating stemness maintenance in HNSCC. These findings suggest that YTHDC1 may serve as a biomarker for stem maintenance and malignant progression in HNSCC, providing new insights into the treatment of cancer.

Intimate intertwining of the pathogenesis of hypoxia and systemic sclerosis: A transcriptome integration analysis.

Systemic sclerosis (SSc) is an autoimmune disease caused by various pathogenic factors, including hypoxia. Hypoxia stimulates the production of the extracellular matrix to promote fibrosis. However, the integrated function and the underlying mechanism of hypoxia in SSc are unclear. In the present study, we used Agilent SurePrint G3 Human Gene Expression v3 for the transcriptional sequencing of fibroblasts with and without hypoxia to detect differentially expressed genes (DEGs) in hypoxia. We analyzed the results with the transcriptome data of SSc lesions (GSE95065) to select the co-DEGs. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the basis of the co-DEGs using the R package ClusterProfiler, which showed that hypoxia and cross talk of hypoxia with other pathogenic factors are involved in the pathogenesis of SSc. Furthermore, we constructed a protein-protein interaction (PPI) network of co-DEGs and screened two significant functional expression modules. We identified nine hub genes (ALDH1A1, EGF, NOX4, LYN, DNTT, PTGS2, TKT, ACAA2, and ALDH3A1). These genes affect the pentose phosphate pathway, oxidative stress, and lipolysis. Our study provides insights into the mechanisms underlying the effects of hypoxia on SSc pathogenesis, which will help to better understand SSc pathogenesis and develop new therapeutic strategies for SSc.

Transcriptome Analysis of Peripheral Blood Mononuclear Cells Response in Patients with Severe COVID-19 Reveals Crucial Genes Regulating Protein Ubiquitination

BackgroundWe sought to further our understanding of the biological characteristics underlying severe COVID-19.Material/MethodsRNA sequencing (RNA-Seq) analysis was used to evaluate peripheral blood mononuclear cells from 4 patients with severe COVID-19 and 4 healthy controls. We performed gene expression analyses to detect differentially expressed genes (DEGs). Enrichment analyses were performed to identify their molecular processes and signaling pathways, and the protein–protein interaction network was constructed to extract the core gene cluster. The investigation of protein-chemical interactions and regulatory signatures for specific regulatory checkpoints and powerful chemical agents was then conducted for these essential genes. Finally, we used single-cell RNA-Seq analysis from an online platform to show how these genes were expressed differently, depending on the kind of cell.ResultsA total of 268 DEGs were found. The biological process of protein ubiquitination was later discovered to be highly influenced by the core gene cluster (ITCH, TRIM21, RNF130, FBXO11, UBE2J1, and ASB16) at the transcriptome level. Six transcription factors, FNIC, FOXA1, YY1, GATA2, MET2A, and FOXC1, as well as miRNAs hsa-miR-1-3p and hsa-miR-27a-3p were identified. We found a potent chemical agent, copper sulfate, may regulate protein ubiquitination genes cooperatively, and the genes regulating protein ubiquitination could be expressed highly on the macrophages.ConclusionsTaken together, we suggest that protein ubiquitination is a crucial functional process in patients with severe COVID-19. This study will give a deeper insight into biological characteristics and progression of COVID-19 and facilitate development of novel therapeutics, leading to significant advancements in the COVID-19 pandemic.

Macrophage polarization regulates intervertebral disc degeneration by modulating cell proliferation, inflammation mediator secretion, and extracellular matrix metabolism.

Macrophage infiltration and polarization have been increasingly observed in intervertebral disc (IVD) degeneration (IDD). However, their biological roles in IDD are still unrevealed. We harvested conditioned media (CM) derived from a spectrum of macrophages induced from THP-1 cells, and examined how they affect nucleus pulposus cells (NPCs) in vitro, by studying cell proliferation, extracellular matrix (ECM) synthesis, and pro-inflammation expression; and in vivo by injection CM in a rat IDD model. Then, high-throughput sequencing was used to detect differentially expressed genes (DEGs). Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) networks were used to further analysis. Higher CCR7+ (M1 marker) and CD206+ (M2 marker) cell counts were found in the degenerated human IVD tissues as compared with the control. Furthermore, the cell co-culture model showed M1CM attenuated NPC proliferation, downregulated the expression of ECM anabolic genes encoding aggrecan and collagen IIα1, upregulated the expression of ECM catabolic genes encoding MMP-13, and inflammation-related genes encoding IL-1β, IL-6, and IL-12, while M2CM showed contrasting trends. In IDD model, higher histological scores and lower disc height index were found following M1CM treatment, while M2CM exhibited opposite results. M1CM injection decreased ECM anabolic and increased ECM catabolic, as well as the upregulation of inflammation-related genes after 8 weeks treatment, while M2CM slowed down these trends. Finally, a total of 637 upregulated and 655 downregulated genes were detected in M1CM treated NPCs, and 975 upregulated genes and 930 downregulated genes in the M2CM groups. The top 30 GO terms were shown and the most significant KEGG pathway was cell cycle in both groups. Based on the PPI analysis, the five most significant hub genes were PLK1, KIF20A, RRM2, CDC20, and UBE2C in the M1CM groups and RRM2, CCNB1, CDC20, PLK1, and UBE2C in the M2CM groups. In conclusion, macrophage polarization exhibited diverse roles in IDD progression, with M1CM exacerbating cell proliferation suppression and IVD degeneration, while M2CM attenuated IDD development. These findings may facilitate the further elucidation of the role of macrophage polarization in IDD, and provide novel insights into the therapeutic potential of macrophages.

Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis.

Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.

Effect of SARS-CoV-2 infection on asthma patients.

BackgroundSARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma.MethodsTo investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates.ResultsThere were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets.ConclusionWe investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

Identification of key pathways and genes in the progression of silicosis based on WGCNA

Silicosis, induced by inhaling silica particles in workplaces, is one of the most common occupational diseases. The prognosis of silicosis and its consequent fibrosis is extremely poor due to limited treatment modalities and lack of understanding of the disease mechanisms. In this study, a Wistar rat model for silicosis fibrosis was established by intratracheal instillation of silica (0, 50, 100 and 200 mg/mL, 1 mL) with the evidence of Hematoxylin and Eosin (HE) and Masson staining and the expressions of inflammatory and fibrotic proteins of rats’ lung tissues. RNA of lung tissues of rats exposed to 200 mg/mL silica particles and normal saline for 14 d and 28 d was extracted and sequenced to detect differentially expressed genes (DEGs) and to identify silicosis fibrosis-associated modules and hub genes by Weighted gene co-expression network analysis (WGCNA). Predictions of gene functions and signaling pathways were conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. In this study, it has been demonstrated the promising role of the Hippo signaling pathway in silicosis fibrosis, which will be conducive to elucidating the specific mechanism of pulmonary fibrosis induced by silica and to determining molecular initiating event (MIE) and adverse outcome pathway (AOP) of silicosis fibrosis.

Identification of inflammatory regulation roles of thalidomide/ruxolitinib in nucleus pulposus and construction of polyelectrolyte nanocomplexes-impregnated injectable hydrogels for synergistic intervertebral disk degeneration treatment

Degeneration of the intervertebral discs (IVD) is an important underlying etiology of degenerative diseases contributor to lower back and neck pain. Alleviation of inflammation is recognized as the most direct way for the treatment of IVD. Repurposing of approved drugs is a viable alternative strategy to de novo drug discovery and development. The aim of this study is to identify the inflammatory regulation roles of the small molecule thalidomide (an immunomodulatory and TNF-α inhibitor) and ruxolitinib (a selective JAK1/2 inhibitor) in nucleus pulposus (NP) cells and investigate the combinational therapeutic potentials of thalidomide and ruxolitinib for ameliorating IVD degeneration. RNA-sequencing (RNA-seq) was used to detect differentially expressed genes (DEGs) after treatment of human NP cells with individual thalidomide and ruxolitinib, and relevant signaling pathways and functions of DEGs were detected by enrichment analysis. Western blot and immunofluorescence were applied to explore the mechanisms by which thalidomide and ruxolitinib regulated inflammatory factors in human NP cells. Moreover, we developed ruxolitinib and thalidomide co-delivered polyelectrolyte nanocomplexes (RTNPs) by the assembly of chitosan derivatives of positively charged chitosan hydrochloride (CS) and negatively charged carboxymethyl chitosan (CMCS). In vitro studies revealed that RTNPs could efficiently deliver drugs into NP cells and corresponding extracellular matrix (ECM)-related genes and proteins were adjusted. Furthermore, The RTNPs were assembled with Pluronic F127 to form a RTNPs/F127 injectable thermoresponsive hydrogels to maintain resident time within the nucleus pulposus, achieve sustained drugs release and achieve long-term therapy effects. The caudal disc degeneration model of SD rats was established by acupuncture, and X-ray, magnetic resonance scanning, μCT scanning, and histological methods were performed to detect the therapeutic effects of RTNPs/F127 hydrogel on IVD in vivo. As was demonstrated in the in vivo studies, in situ intradiscal injection of RTNPs/F127 composite displayed a relatively slower disc degenerating progression in a puncture-induced IVD rat model compared with the monotherapy pattern.

TPX2 Serves as a Cancer Susceptibility Gene and Is Closely Associated with the Poor Prognosis of Endometrial Cancer.

Background Endometrial cancer (EC) is a common tumor of the genital tract that affects the female reproductive system but with only limited treatment options. We aimed to discover new prognostic biomarkers for EC. Methods We used mRNA-seq data to detect differentially expressed genes (DEGs) between EC and control tissues. Detailed clinicopathological information was collected, and changes in the mRNA and protein levels of hub DEGs were analyzed in EC. Copy number variation (CNV) was also evaluated for its association with the pathogenesis of EC. Gene set enrichment analysis (GSEA) was conducted to enrich significant pathways driven by the hub genes. Cox regression analysis was used to select variables to create a nomogram. The nomogram was calibrated by applying the concordance index (C-index), and net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). Results Differential expression analysis identified 24 DEGs as potential risk factors for EC. Survival analysis revealed that TPX2 expression was related to worsening overall survival in patients with advanced EC. A high CNV was associated with the overexpression of TPX2; this suggested that modifications in the cell-cycle pathway might be crucial in the advancement of EC. Moreover, an individualized nomogram was developed for TPX2 incorporating clinical factors; this was also evaluated for its ability to predict EC. Calibration and DCA analyses confirmed the robustness and clinical usefulness of the nomogram. Conclusion We offer novel insights into the pathogenesis and molecular mechanisms of EC. The overexpression of TPX2 was related to a poorer prognosis and could serve as a biomarker for predicting prognostic outcomes in EC patients.

Identification of Hub Genes and Biological Pathways in Inclusion Body Myositis Using Bioinformatics Analysis

BackgroundInclusion body myositis (IBM) is a unique idiopathic inflammatory myopathy with unclear pathogenesis and poor prognosis. Although previous publications have identified some molecular biomarkers, the value of these biomarkers is unknown.ObjectiveTo identify hub genes and signaling pathways related to IBM for understanding the IBM-related mechanisms and providing guidance for therapy development.MethodsTwo microarray datasets (GSE3112 and GSE128470) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to detect differentially expressed genes (DEGs) between IBM and normal muscle tissues. The hub genes were determined using protein–protein interaction (PPI) network in Cytoscape. The specific signaling pathways and biological functions of IBM were identified using GO, KEGG, and GSEA enrichment analyses. Moreover, CIBERSORT was applied to estimate the expression level of 22 immune cell types in IBM and normal muscle tissue. The relationship between the immune cell types and hub genes was then explored.ResultsA total of 219 DEGs and 10 hub genes were identified. Enrichment analyses revealed that the chemokine signaling pathway, cellular response to interferon-gamma, and P53 pathway have crucial roles in IBM. Immune infiltration analyses showed that IBM was associated with high level of CD8 T cells, Tregs, and macrophages. Finally, five potential drugs were predicted for IBM patients through CMap (connectivity map) database.ConclusionIn this study, the underlying molecular mechanisms and immunological landscape of IBM were investigated, and thus may provide new directions for future research on IBM pathogenesis.