Collagen-containing Extracellular Matrix Research Articles

BZW1 as an oncogene is associated with patient prognosis and the immune microenvironment in glioma

BackgroundGlioma is the most common primary tumor in the human central nervous system. This study was designed to explore the expression of BZW1 in glioma and its relevance to the clinicopathological features and outcome of glioma patients. MethodsGlioma transcription profiling data were obtained from The Cancer Genome Atlas (TCGA). TIMER2, GEPIA2, GeneMANIA, and Metascape were searched in the present study. Cell and animal experiments were conducted to verify the effect of BZW1 on glioma cell migration in vitro and in vivo. Transwell assays, western blotting and immunofluorescence assays were performed. ResultsWe found that BZW1 was highly expressed in gliomas and correlated with poor prognosis. BZW1 could promote glioma proliferation. GO/KEGG analysis revealed that BZW1 was involved in collagen-containing extracellular matrix and was correlated with ECM-receptor interactions, transcriptional misregulation in cancer and the IL-17 signaling pathway. In addition, BZW1 was also associated with the glioma tumor immune microenvironment. ConclusionBZW1 can promote glioma proliferation and progression, and its high expression is correlated with a poor prognosis. BZW1 is also associated with the tumor immune microenvironment of glioma. This study may facilitate further understanding of the critical role of BZW1 in human tumors, including gliomas.

MMP2 is a immunotherapy related biomarker and correlated with cancer-associated fibroblasts infiltrate in melanoma

BackgroundMounting evidence supports that matrix metalloproteinase (MMPs) are highly associated with tumor progression and that targeting MMPs may overcome the barrier of immune suppression. Among these, whether MMP2 functions as an immunosuppressive role in melanoma, remains unclear.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases were used to assess the prognosis of MMP2 in melanoma, after which Tumor immune estimation resource (TIMER) was used to explore the relationship between MMP2 expression and cancer associated fibroblasts (CAFs) infiltration. Finally, we evaluated the efficacy of MMP2 inhibitor on CAFs infiltration and immunotherapy using a mouse melanoma model.ResultsIn general, the expression of MMP2, MMP13, MMP16, MMP17 and MMP25 were significantly associated with skin cutaneous melanoma (SKCM) patients prognosis, among which MMP2 low expression benefited patients the most. Especially, the overall survival (OS) of BRAF mutation patients with high MMP2 expression was significantly lower than the MMP2 low expression group, but there was no significant difference in BRAF wild-type patients. KEGG and GO enrichment analysis indicated that MMP2 related genes were mostly associated with extracellular structure organization, collagen-containing extracellular matrix and extracellular matrix structural constituent. Furthermore, in almost all cancers, MMP2 expression was positively correlated with CAFs infiltration. MMP2 inhibitor works synergistically with PD-1 antibody and induces tumor regression in a mouse melanoma model, which is dependent on decreased CAFs infiltration.ConclusionsThis suggests that MMP2 plays a vital role in the regulation of CAFs infiltration, potentially participating in immunotherapy response, and thus representing a valuable target of immunotherapy in melanoma.

SLAMF7/STAT6 Pathway Inhibits Innate Immune Response in Late-Stage Human Acanthamoeba Keratitis: A Comparative Transcriptome Analysis

Acanthamoeba keratitis (AK) is a blinding corneal infection caused by the protozoan Acanthamoeba. The long-term course of AK suggests the host immunity could not kill Acanthamoeba rapidly. The immune status is still unclear in the late stage of AK. The comparative transcriptome analysis was made based on the bulk RNA sequencing of cornea tissues from AK patients and donors. Differentially expressed genes and enriched signaling pathways were calculated. CIBERSORT algorithm was used for immune infiltration analysis of cornea tissue between AK and normal controls. A total of 2668 differentially expressed genes, including 1477 upregulated genes and 1191 downregulated genes, were detected. Gene Ontology analysis revealed that the pathways were significantly enriched in leukocyte migration, regulation of T-cell activation, the external side of plasma membrane, collagen-containing extracellular matrix, immune receptor activity, and cytokine binding. The Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the pathways were significantly enriched in the cytokine-cytokine receptor interaction, hematopoietic cell lineage, and Staphylococcus aureus infection pathway. The immune infiltration profiles varied little between AK and normal controls. Compared with normal tissue, cornea tissue of AK contained a higher proportion of M0 macrophages and CD8 T cells, while resting memory CD4 T cells contributed to a relatively lower portion (p < 0.05). Finally, the expression levels of cell markers and SLAMF7/STAT6 pathway were confirmed by histopathology examinations, RT-qPCR, and Western blot.

Seven basement membrane-specific expressed genes are considered potential biomarkers for the diagnosis and treatment of diabetic nephropathy.

Diabetic nephropathy (DN) is a diabetes-related chronic vasculitis. DN diminishes kidney function over time and, of course, leads to end stage renal disease in people (ESRD). In spite of the advances in diagnostic and treatment methods for DN, DN continues to impose a significant physical and psychological burden on patients, severely impacting their quality of life, making the hunt for novel therapeutic targets necessary. The Gene Expression Omnibus (GEO) microarray datasets GSE1009, GSE30122, GSE142153, and GSE96804 were downloaded to identify differentially expressed genes (DEGs) in kidney tissues from patients in the DN group and normal controls. These three datasets were examined for genes associated with basement membranes (BMs) with differential gene expression. The target genes were then subjected to gene ontology (GO) annotation and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis. BM-related genes underwent PPI network analysis and screening of the top 10 hub genes, along with immune infiltration analysis and column line graph model development. Finally, we conducted DN therapeutic medication prediction and the creation of something like a miRNA network for genetic markers with BMs. Seven candidate BM-related genes (COL4A1, COL4A2, COL6A2, COL6A3, FN1, ITGQ4, and LAMB1) with acceptable helps the healthcare were discovered. Enrichment analysis of diabetes-related genes event occurred the role of biological processes including extracellular matrix organization, extracellular structural organization, and collagen-containing extracellular matrix, as well as the PI3K-Akt signaling pathway and the AGE-RAGE signaling pathway, in diabetic complications. These genes may also be associated in immune cells and autoimmune activities, such as Macrophages and MHC class I, in order to impact the immune process in DN. In the meanwhile, based on these seven BM-related genes, we discovered that Ginsenoside Rh1 was very significant for drug targeting. This research identified seven BM-related genes as possible diagnostic and therapeutic biomarkers for DN. Analysis of inflammatory infiltration indicated that these genes may be important in inflammatory processes through Macrophages and MHC class I, hence impacting the course and development of DN illness. The development of a correlated column line graph model for it also shown excellent predictive capabilities. In addition, we have found pharmaceuticals, such as Ginsenoside Rh1, that may provide fresh insights into the personalized management of patients with DN.

Integrative transcriptomics analysis of early-onset and late-onset colorectal cancer

Colorectal cancer (CRC) represents the third most prevalent form of cancer, comprising approximately 7% of all cancer varieties. The primary objective of this study is to pinpoint and scrutinize the differentially expressed genes (DEGs) present in early-onset CRC (EOCRC) as compared to late-onset CRC (LOCRC). For our analysis, we retrieved RNA-seq data from the GEO database, subsequently employing the GEO2R tool for its evaluation. This step was followed by an extensive process of gene and pathway enrichment, identification of protein-protein interactions, and the prediction of pivotal transcriptional factors. Additionally, we examined survival rates and responses to chemotherapy treatments. We identified 250 DEGs, of which 235 were down-regulated and 15 were up-regulated. The most prevalent categories identified were extracellular structure organization in biological processes, collagen-containing extracellular matrix in cellular compartments, platelet-derived growth factor in molecular functions, and protein digestion and absorption in KEGG pathways. Furthermore, we isolated ten central hub genes: COL1A1, VWF, COL3A1, EGF, IGF1, COL1A2, ITGB3, COL11A2, COL6A1, and CD163. Among these, COL1A1 and EGF stand out; COL1A1 serves as a predictive biomarker, while EGF holds potential as a prognostic biomarker. Additionally, our predictive models highlight FOXC1, GATA2, YY1, TFAP2A, and PPARG as the most influential transcriptional factors governing these hub genes. In summation, our findings suggest that these hub genes play a crucial role in differentiating EOCRC from LOCRC, warranting further comprehensive investigation.

Identification of Signature Genes in the PD-1 Relative Gastric Cancer Using a Combined Analysis of Gene Expression and Methylation Data.

The morbidity and mortality rates for gastric cancer (GC) rank second among all cancers, indicating the serious threat it poses to human health, as well as human life. This study aims to identify the pathways and genes as well as investigate the molecular mechanisms of tumor-related genes in gastric cancer (GC). We compared differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in gastric cancer and normal tissue samples using The Cancer Genome Atlas (TCGA) data. The Kyoto Encyclopedia of Gene and Genome (KEGG) and the Gene Ontology (GO) enrichment analysis' pathway annotations were conducted on DMGs and DEGs using a clusterProfiler R package to identify the important functions, as well as the biological processes and pathways involved. The intersection of the two was chosen and defined as differentially methylated and expressed genes (DMEGs). For DMEGs, we used the principal component analysis (PCA) to differentiate gastric cancer from adjacent samples. The linear discriminant analysis method was applied to categorize the samples using DMEGs methylation data and DMEGs expression profiles data and was validated using the leave-one-out cross-validation (LOOCV) method. We plotted the ROC curve for the classification and calculated the AUC (area under the ROC curve) value for a more intuitive view of the classification effect. We also used the NetworkAnalyst 3.0 tool to analyze DMEGs, using DrugBank to acquire information on protein-drug interactions and generate a network map of gene-drug interactions. We identified a total of 971 DMGs in 188 PD-1 negative and 187 PD-1 positive gastric cancer samples obtained from TCGA. The KEGG and GO enrichment analysis showed the involvement of the regulation of ion transmembrane transport, collagen-containing extracellular matrix, cell-cell junction, and peptidase regulator activity. We simultaneously obtained 1,189 DEGs, out of which 986 were downregulated, while 203 were upregulated in tumors. The enriched analysis of the GO's and KEGG's pathways indicated that the most significant pathways included an intestinal immune network for IgA production, Staphylococcus aureus infection, cytokine-cytokine receptor interaction, and viral protein interaction with cytokine and cytokine receptor, which have previously been linked with gastric cancer. The compound DB01830 can bind well to the active site of the LCK protein and shows good stability, thus making it a potential inhibitor of the LCK protein. To observe the relationship between DMEGs' expression and prognosis, we observed 10 genes, among which were TRIM29, TSPAN8, EOMES, PPP1R16B, SELL, PCED1B, IYD, JPH1, CEACAM5, and RP11-44K6.2. Their high expressions were related to high risks. Besides, those genes were validated in different internal and external validation sets. These results may provide potential molecular biological therapy for PD-1 negative gastric cancer.

The new ceRNA crosstalk between mRNAs and miRNAs in intervertebral disc degeneration.

Degeneration of the intervertebral disc has been linked to lower back pain. To date, pathophysiological mechanisms of intervertebral disc degeneration (IDD) remain unclear; it is meaningful to find effective diagnostic biomarkers and new therapeutic strategies for IDD. This study aimed to reveal the molecular mechanism of IDD pathogenesis from the multidimensional transcriptomics perspective. Here, we acquired IDD bulk omics datasets (GSE67567 and GSE167199) including mRNA, microRNA expression profiles, and single-cell RNA sequencing (GSE199866) from the public Gene Expression Omnibus (GEO) database. Through principal component analysis and Venn analysis, we found different expression patterns in the IDD transcription level and identified 156 common DEGs in both bulk datasets. GO and KEGG functional analyses showed these dysregulators were mostly enriched in the collagen-containing extracellular matrix, cartilage development, chondrocyte differentiation, and immune response pathways. We also constructed a potentially dysregulated competing endogenous RNA (ceRNA) network between mRNAs and miRNAs related to IDD based on microRNA target information and co-expression analysis of RNA profiles and identified 36 ceRNA axes including ZFP36/miR-155-5p/FOS, BTG2/hsa-miR-185-5p/SOCS3, and COL9A2/hsa-miR-664a-5p/IBA57. Finally, in integrating bulk and single-cell transcriptome data analyses, a total of three marker genes, COL2A1, PAX1, and ZFP36L2, were identified. In conclusion, the key genes and the new ceRNA crosstalk we identified in intervertebral disc degeneration may provide new targets for the treatment of IDD.

A novel in vitro prognostic model of bladder cancer based on urine-derived living tumor cells

Bladder cancer (BLCA) remains a difficult malignancy to manage because of its high recurrence, intense follow-up, and invasive diagnostic and treatment techniques. Immune checkpoint inhibitors (ICIs) have forged a new direction for the treatment of BLCA, but it is currently challenging to predict whether an individual patient will be sensitive to ICIs. We collected 43 urine/tumor samples from BLCA patients for primary bladder cancer cells (BCCs) culturing using our previously reported BCC culture platform. We used flow cytometry (FCM) to measure the expression levels of Programmed Death-Ligand 1 (PD-L1) on BCCs before and after interferon-gamma (IFN-γ) treatment and found that PD-L1 expression and the sensitivities to IFN-γ varied among patients. RNA-sequencing, western blotting, and programmed death-1 (PD-1) binding assays confirmed that the BCC FCM-based PD-L1 detection platform (BC-PD-L1) was reliable and was not hindered by the glycosylation of PD-L1. In the subsequent retrospective study, we found that IFN-γ-stimulated PD-L1 (sPD-L1) expression on BCCs detected by BC-PD-L1 could predict the prognosis of BLCA patients. Importantly, the prognostic value was similar or even better in urine-derived BC-PD-L1 (UBC-PD-L1). Transcriptome analysis showed that BCCs with high sPD-L1 tended to enrich genes associated with the collagen-containing extracellular matrix, cell–cell adhesion, and positive regulation of the immune system. In addition, the UBC-PD-L1 also exhibited predictive value for ICI response in BLCA patients. In conclusion, as a novel personalized urine-detection method, UBC-PD-L1 may provide a rapid, accurate, and non-invasive tool for monitoring tumor progression, predicting therapeutic responses, and helping improve BLCA clinical treatment in future.

Single-cell profiling of mouse and primate ovaries identifies high levels of EGFR for stromal cells in ovarian aging

Increased ovarian fibrosis and an expanded stromal cell compartment are the main characteristics of aging ovaries. However, the molecular mechanisms and the key factor of stromal cells underlying ovarian aging remain unclear. Here, we explored single-cell transcriptomic data of ovaries from the adult mouse (4,363 cells), young (1,122 cells), and aged (1,479 cells) non-human primates (NHPs) to identify expression patterns of stromal cells between young and old ovaries. An increased number of stromal cells (p= 0.0386) was observed in aged ovaries of NHPs, with enrichment processes related to the collagen-containing extracellular matrix. In addition, differentially expressed genes of stromal cells between young and old ovaries were regulated by ESR1 (p= 7.94E-08) and AR (p= 1.99E-05). Among them, EGFR was identified as the common target and was highly expressed (p= 7.69E-39) in old ovaries. In human ovaries, the correlated genes of EGFR were associated with the process of the cell-substrate junction. Silencing of EGFR in human ovarian stromal cells led to the reduction of cell-substrate junction via regulating phosphorylation modification of the AKT-mTOR signaling pathway and stromal cell marker genes. Overall, we identified high levels of EGFR for stromal cells in ovarian aging, which provides insight into the cell type-specific molecular mechanisms underlying ovarian aging at single-cell resolution.

LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in stomach adenocarcinoma.

The potential application value of Long non-coding RNA disrupted in renal carcinoma 1 (DIRC1) has not yet been explored, the purpose of this study was to explore the relationship between DIRC1 and stomach adenocarcinoma (STAD) based on the cancer genome atlas database. Wilcoxon rank sum test, Chi-square test, Fisher test and logistic regression were used to evaluate relationships between clinical-pathologic features and DIRC1 expression. Receiver operating characteristic (ROC) curves were used to describe binary classifier value of DIRC1 using area under curve (AUC) score. Kaplan-Meier method was used to assess the impact of DIRC1 on prognosis and the impact of DIRC1-related hub genes on prognosis. Gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to predict the function of differentially expressed genes associated with DIRC1. Gene set enrichment analysis (GSEA) was used to predict biological states or processes associated with DIRC1. Immune infiltration analysis was performed to identify the significantly involved functions of DIRC1. Protein-protein interaction (PPI) networks were established and 10 hub genes identified with Cytoscape software. Real time-polymerase chain reaction (RT-PCR) was used to detect the expression of DIRC1 in Gastric Cancer patients and healthy people. Increased DIRC1 expression in STAD was associated with T stage (P = .004), race (P = .045), histologic grade (P = .029) and anatomic neoplasm subdivision (P = .034). ROC curve suggested the significant diagnostic ability of DIRC1 (AUC = 0.779). High DIRC1 expression predicted a poorer Overall survival (P = .004, hazard ratio: 1.63; 95% confidence interval: 1.17-2.27; P = .034). GO and KEGG analysis demonstrated that DIRC1 is related to epidermis, collagen-containing extracellular matrix, receptor-ligand activity, protein digestion and absorption, etc. GSEA demonstrated that E2F target, G2M checkpoint, Myc target, interferon γ reaction were differentially enriched in the high DIRC1 expression phenotype. SsGSEA and Spearman correlation revealed the relationships between DIRC1 and macrophages, dendritic cells, and Th1 cells were the strongest. Coregulatory proteins were included in the PPI network, higher expressions of 4 hub genes were associated with worse prognosis in STAD. RT-PCR showed that the expression of DIRC1 in the serum of Gastric Cancer patients was higher than healthy people (P = .027). DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.

Identification of functional TF-miRNA-hub gene regulatory network associated with ovarian endometriosis.

Endometriosis (EMs), one of the most common gynecological diseases, seriously affects the health and wellness of women; however, the underlying pathogenesis remains unclear. This study focused on dysregulated genes and their predicted transcription factors (TFs) and miRNAs, which may provide ideas for further mechanistic research. The microarray expression dataset GSE58178, which included six ovarian endometriosis (OE) samples and six control samples, was downloaded from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to study the cellular and organism-level functions of DEGs. The protein-protein interaction (PPI) network was built and visualized using Cytoscape, and modules and hub genes were explored using various algorithms. Furthermore, we predicted miRNAs and TFs of hub genes using online databases, and constructed the TF-miRNA-hub gene network. There were 124 upregulated genes and 66 downregulated genes in EMs tissues. GO enrichment analysis showed that DEGs were concentrated in reproductive structure development and collagen-containing extracellular matrix, while KEGG pathway analysis showed that glycolysis/gluconeogenesis and central carbon metabolism in cancer require further exploration. Subsequently, HIF1A, LDHA, PGK1, TFRC, and CD9 were identified as hub genes, 22 miRNAs and 34 TFs were predicted to be upstream regulators of hub genes, and these molecules were pooled together. In addition, we found three key feedback loops in the network, MYC-miR-34a-5p-LDHA, YY1-miR-155-5p-HIF1A, and RELA-miR-93-5p-HIF1A, which may be closely related to OE development. Taken together, our study structured a TF-miRNA-hub gene network to decipher the molecular mechanism of OE, which may provide novel insights for clinical diagnosis and treatment.

Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation.

ObjectiveAbdominal aortic aneurysm (AAA) is a lethal peripheral vascular disease. Inflammatory immune cell infiltration is a central part of the pathogenesis of AAA. It’s critical to investigate the molecular mechanisms underlying immune infiltration in early-stage AAA and look for a viable AAA marker.MethodsIn this study, we download several mRNA expression datasets and scRNA-seq datasets of the early-stage AAA models from the NCBI-GEO database. mMCP-counter and CIBERSORT were used to assess immune infiltration in early-stage experimental AAA. The scRNA-seq datasets were then utilized to analyze AAA-related gene modules of monocytes/macrophages infiltrated into the early-stage AAA by Weighted Correlation Network analysis (WGCNA). After that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis for the module genes was performed by ClusterProfiler. The STRING database was used to create the protein-protein interaction (PPI) network. The Differentially Expressed Genes (DEGs) of the monocytes/macrophages were explored by Limma-Voom and the key gene set were identified. Then We further examined the expression of key genes in the human AAA dataset and built a logistic diagnostic model for distinguishing AAA patients and healthy people. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and Enzyme Linked Immunosorbent Assay (ELISA) were performed to validate the gene expression and serum protein level between the AAA and healthy donor samples in our cohort.ResultsMonocytes/macrophages were identified as the major immune cells infiltrating the early-stage experimental AAA. After pseudocell construction of monocytes/macrophages from scRNA-seq datasets and WGCNA analysis, four gene modules from two datasets were identified positively related to AAA, mainly enriched in Myeloid Leukocyte Migration, Collagen-Containing Extracellular matrix, and PI3K-Akt signaling pathway by functional enrichment analysis. Thbs1, Clec4e, and Il1b were identified as key genes among the hub genes in the modules, and the high expression of Clec4e, Il1b, and Thbs1 was confirmed in the other datasets. Then, in human AAA transcriptome datasets, the high expression of CLEC4E, IL1B was confirmed and a logistic regression model based on the two gene expressions was built, with an AUC of 0.9 in the train set and 0.79 in the validated set. Additionally, in our cohort, we confirmed the increased serum protein levels of IL-1β and CLEC4E in AAA patients as well as the increased expression of these two genes in AAA aorta samples.ConclusionThis study identified monocytes/macrophages as the main immune cells infiltrated into the early-stage AAA and constructed a logistic regression model based on monocytes/macrophages related gene set. This study could aid in the early diagnostic of AAA.

Identification of Key Genes and Molecular Pathways in Keratoconus: Integrating Text Mining and Bioinformatics Analysis.

Purpose To identify the potential key genes and molecular pathways associated with keratoconus and allergic disease. Methods The pubmed2ensembl database was used to identify the text mining genes (TMGs) collectively involved in keratoconus and allergic disease. The GeneCodis program was used to perform the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of TMGs. The protein-protein interaction (PPI) network of the TMGs was established by STRING; the significant gene modules and hub genes of PPI were further performed using the Cytoscape software. The DAVID database was used to perform the GO and KEGG analyses of the significant module. Results In total, 98 TMGs collectively involved in keratoconus and allergic disease were identified. 19 enriched biological processes including 71 genes and 25 enriched KEGG pathways including 59 genes were obtained. A TMG PPI network was constructed, and 51 genes/nodes were identified with 110 edges; 3 most significant modules and 12 hub genes were chosen from the PPIs. GO enrichment analysis showed that the TMGs were primarily associated with collagen catabolic process, extracellular matrix organization and disassembly, cell adhesion and migration, collagen-containing extracellular matrix, extracellular matrix, and structure organization. KEGG pathway analysis showed that these DEGs were mainly involved in the IL-17 signaling pathway, inflammatory bowel disease, rheumatoid arthritis, allograft rejection, T cell receptor signaling pathway, cytokine-cytokine receptor interaction, and TNF signaling pathway. Conclusions The results revealed that IL10, IL6, MMP9, MMP1, HGF, VEGFA, MMP3, MMP2, TGFB1, IL4, IL2, and IFNG were potential key genes involved in keratoconus. IL-17 signaling pathway was the potential pathways accounting for pathogenesis and development of keratoconus.

Integrative transcriptional characterization of cell cycle checkpoint genes promotes clinical management and precision medicine in bladder carcinoma.

BackgroundThe aberrant regulation of cell cycle is significantly correlated with cancer carcinogenesis and progression, in which cell cycle checkpoints control phase transitions, cell cycle entry, progression, and exit. However, the integrative role of cell cycle checkpoint-related genes (CRGs) in bladder carcinoma (BC) remains unknown.MethodsThe transcriptomic data and clinical features of BC patients were downloaded from The Cancer Genome Atlas (TCGA), used to identify CRGs correlated with overall survival (OS) by univariate Cox regression analysis. Then, the multivariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses further developed a prognostic CRG signature, which was validated in three external datasets retrieved from Gene Expression Omnibus (GEO). The receiver operating characteristic curve (ROC) analysis was conducted for evaluating the performance of the CRG signature in prognosis prediction. RNA sequencing (RNA-Seq) was performed to explore the expression difference in the identified CRGs between tumor and normal tissue samples from 11 BC patients in the local cohort. Ultimately, genomic profiles and tumor microenvironment (TME), and the Genomics of Drug Sensitivity in Cancer (GDSC) were investigated to guide precision treatment for BC patients with different CRG features.ResultsThe novel constructed 23-CRG prognostic signature could stratify BC patients into high-risk and low-risk groups with significantly different outcomes (median OS: 13.64 vs. 104.65 months). Notably, 19 CRGs were the first to be identified as being associated with BC progression. In three additional validation datasets (GSE13507, GSE31684, and GSE32548), higher CRG scores all indicated inferior survival, demonstrating the robust ability of the CRG signature in prognosis prediction. Moreover, the CRG signature as an independent prognostic factor had a robust and stable risk stratification for BC patients with different histological or clinical features. Then, a CRG signature-based nomogram with a better performance in prognostic prediction [concordance index (C-index): 0.76] was established. Functional enrichment analysis revealed that collagen-containing extracellular matrix (ECM), and ECM-related and MAPK signaling pathways were significantly associated with the signature. Further analysis showed that low-risk patients were characterized by particularly distinctive prevalence of FGFR3 (17.03% vs. 6.67%, p < 0.01) and POLE alterations (7.97% vs. 2.50%, p < 0.05), and enrichment of immune infiltrated cells (including CD8+ T cells, CD4+ naïve T cells, follicular helper T cells, Tregs, and myeloid dendritic cells). RNA-seq data in our local cohort supported the findings in the differentially expressed genes (DEGs) between tumor and normal tissue samples, and the difference in TME between high-risk and low-risk groups. Additionally, CRG signature score plus FGFR3 status divided BC patients into four molecular subtypes, with distinct prognosis, TME, and transcriptomic profiling of immune checkpoint genes. Of note, CRG signature score plus FGFR3 status could successfully distinguish BC patients who have a higher possibility of response to immunotherapy or chemotherapy drugs.ConclusionsThe CRG signature is a potent prognostic model for BC patients, and in combination with FGFR3 alterations, it had more practical capacity in the prediction of chemotherapy and immunotherapy response, helping guide clinical decision-making.

Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis

ObjectiveThis study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets.MethodsThree transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks.ResultsA total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors.ConclusionsWe screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets.

Expression characteristics of long non-coding RNA in colon adenocarcinoma and its potential value for judging the survival and prognosis of patients: bioinformatics analysis based on The Cancer Genome Atlas database

To investigate the expression characteristics of long non-coding RNA (lncRNA) in colon adenocarcinoma (COAD) and its potential value in predicting the prognosis of patient survival. We downloaded COAD-related RNA sequencing (RNA-seq) data and patient survival data from The Cancer Genome Atlas (TCGA). The data were analyzed for lncRNA expression differences, subjected to Cox regression analysis for survival rate, and Kaplan-Meier (KM) survival curves were plotted to analyze the role of the key genes related to prognostic survival by pathway enrichment analysis. The data of 494 COAD clinical samples from TCGA were analyzed; 204 lncRNAs were differentially expressed, 156 were up-regulated, and 48 were down-regulated. The 10 genes with the most significant expression differences were Linc02418, Blacat1, ELFN1-AS1, CRNDE, AC002384.1, AL353801.1, LINC01645, AC073283.2, AC087379.1, and LINC00484. Cox regression analysis of 204 lncRNA genes showed that 23 lncRNA genes with significant effects on the prognosis and survival rate of COAD patients were obtained when P<0.05 was used as the threshold. With P≤0.001 as the threshold, the KM curves of 4 genes (Linc02257, Linc02474, Ac010789.1, Ac083967.1) were statistically significant (P<0.05). The gene Linc02257 was selected for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and it was revealed that the inheritance of Linc02257-regulated gene expression was closely related to tumor development, such as collagen-containing extracellular matrix, organogenesis, activity of membrane protein receptors, and ion channel activity. The signaling pathways regulated by Linc02257 were also closely related to tumors, such as neuroactive ligand-receptor interaction, the PI3K-AKT signaling pathway, calcium signaling pathway, and protein digestion and absorption. In COAD, lncRNA is differentially expressed and plays an important role in the disease regulation. It has potential application value in the diagnosis, targeted therapy, and prognosis of COAD patients.

Correlation of NTRK1 Downregulation with Low Levels of Tumor-Infiltrating Immune Cells and Poor Prognosis of Prostate Cancer Revealed by Gene Network Analysis.

Prostate cancer (PCa) is a life-threatening heterogeneous malignancy of the urinary tract. Due to the incidence of prostate cancer and the crucial need to elucidate its molecular mechanisms, we searched for possible prognosis impactful genes in PCa using bioinformatics analysis. A script in R language was used for the identification of Differentially Expressed Genes (DEGs) from the GSE69223 dataset. The gene ontology (GO) of the DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. A protein–protein interaction (PPI) network was constructed using the STRING online database to identify hub genes. GEPIA and UALCAN databases were utilized for survival analysis and expression validation, and 990 DEGs (316 upregulated and 674 downregulated) were identified. The GO analysis was enriched mainly in the “collagen-containing extracellular matrix”, and the KEGG pathway analysis was enriched mainly in “focal adhesion”. The downregulation of neurotrophic receptor tyrosine kinase 1 (NTRK1) was associated with a poor prognosis of PCa and had a significant positive correlation with infiltrating levels of immune cells. We acquired a collection of pathways related to primary PCa, and our findings invite the further exploration of NTRK1 as a biomarker for early diagnosis and prognosis, and as a future potential molecular therapeutic target for PCa.

Increased expression of tribbles homolog 3 predicts poor prognosis and correlates with tumor immunity in clear cell renal cell carcinoma: a bioinformatics study

ABSTRACT Tribbles homolog 3 (TRIB3), a pseudokinase that regulates multiple intracellular signaling pathways, has been reported to promote the growth of multiple tumors. However, its role in clear cell renal cell carcinoma (ccRCC) remains unelucidated. We evaluated the role of TRIB3 in ccRCC using publicly available data from The Cancer Genome Atlas and analyzed its relationship with the tumor microenvironment; moreover, we used gene knockout and overexpression techniques to detect the effects of TRIB3 on the biological behavior of ccRCC cells. RT-qPCR and western blotting were used to detect transfection efficiency, and the invasiveness of ccRCC cells was determined by Transwell migration assays. We found that TRIB3 overexpression was significantly associated with increased grade, stage, and distant metastasis, positively correlated with ccRCC invasiveness, and also an independent risk factor for overall survival (OS). In addition, 361 differentially expressed genes (DEGs) related to TRIB3 were identified. Functional enrichment analysis showed that DEGs were mainly enriched in humoral immune responses, collagen-containing extracellular matrix, and serine hydrolase activity. Immune landscape characterization revealed that TRIB3 expression was significantly and negatively associated with CD8+ T and hematopoietic stem cells, whereas it was positively associated with NK T and macrophage M1 cells. Single-cell sequencing showed that localization and binding targets of TRIB3 mainly involved monocytes/macrophages and CD4+ and CD8+ T cells. Overall, our study revealed that elevated TRIB3 expression represents a promising prognostic marker for ccRCC patients and may play a key role in tumor microenvironment modulation.

Metabolic trade‐off and adipose tissue role in Neonatal Sepsis

BackgroundNeonatal sepsis contributes substantially to neonatal morbidity and mortality. The activation of an inflammatory response by the host triggers a significant metabolic challenge in which mobilization of stored energy is redistributed. Metabolic costs of immunity are high and physiological trade‐offs that neonates suffering from sepsis undergoes can determine the disease tolerance. Adipose tissue (AT) is a highly dynamic endocrine organ that partakes inter‐organs communication and regulates metabolism. In this study, we investigated whether immunity activation due to distinct TLRs ligands in neonatal sepsis promoted an energetic trade‐off and if the AT depots promoted tissue tolerance.MethodsPostnatal day 5 (PD5) CD1 mice were I.P. treated either with saline (Control), LPS, or Pam3CSK4 (PAM). Body weight and temperature were accessed prior treatments and after 3 hours. Oxygen and CO2 consumption was assessed through Comprehensive Lab Animal Monitoring System (CLAMS). Following euthanasia, blood was collected and subcutaneous white AT (WAT) and brown AT (BAT) depots were dissected out and submitted to microarray analysis utilizing Clariom™ S Assay mouse and the Affymetrix GeneChip Mouse Gene ST 2.0 arrays. Blood glucose was assessed using a glucometer.ResultsThe activation of inflammation promoted either by LPS or PAM resulted in significantly reduced total body weight due to the treatment only after 3 hours. We asked if this effect was due to the growth or nutritional status, we found the control pups kept with the mom increased body weight, whereas the pups fasting did not, as expected. However, these changes were not observed in the TLR4 and TLR1/2 ligands treated pups, in which, the bodyweight was decreased independently of them being fasted or not. The cutaneous temperature remained similar in the control group, while LPS induced a loss of ‐1.5°C degrees post‐treatment, and only ‐0.5°C variation was observed in the PAM treated pups. A reduction in VO2 and VCO2 consumption was stronger in LPS than in PAM treated pups, in which they started to increase these levels after 90 minutes, indicating that TLR4 induced energy conservation also in neonates. LPS and PAM induced diminished blood glucose levels compared to control in fasting pups, and only in LPS in fed pups. Next, we evaluated the contribution of AT to lower metabolic rate and promote energy conservation. Microarray analysis showed LPS induced a total of 103 up‐regulated genes, and 238 due to PAM in BAT. Enrichment analysis of this depot was related to Collagen‐containing extracellular matrix, and Inflammatory response in LPS treated pups, while mRNA metabolic process was highlighted in pups treated with PAM. Moreover, in the WAT, LPS promoted enrichment of inflammatory responses along with lipid storage, fatty acid‐binding and lipid metabolism, whereas PAM induced several pathways related to lipid storage, catabolism, and temperature homeostasis.ConclusionsCollectively, these data highlight the physiological trade‐offs occurring between immunity and energy suggesting that the hypometabolic‐hypothermic state is inducing energy conservation. AT depots are promoting fatty acid oxidation and perhaps mediating this energetic trade‐off. Thus, these physiological outcomes demonstrate the importance of AT regulation over the course of neonatal sepsis.

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning.

BackgroundBreast cancer is one of the most common malignant tumors among women worldwide. This study aimed to screen key genes and pathways for breast cancer diagnosis and treatment.Material/MethodsWe obtained public data from the NCBI GEO database. The data were divided into a control group (normal breast tissue) and a treatment group (breast cancer tissue). We screened 32 differentially expressed genes (DEGs) between normal breast and cancerous tissues and used GO analysis and GSEA to identify the key pathways. We then combined LASSO and SVM-RFE analyses to screen key genes, and used CIBERSORT to obtain the proportion of 22 types of immune cells. The relationships between key genes and immune-infiltrating cells were further explored.ResultsWe screened 32 DEGs from the 2 groups, including 27 downregulated genes and 5 upregulated genes. GO analysis indicated that the DEGs were mainly correlated with collagen-containing extracellular matrix (ECM), Wnt signaling pathway, and glycosaminoglycan binding. GSEA indicated that the treatment group was correlated with chromosome segregation and cell cycle while the control group was correlated with cornification, intermediate filament, and nuclear transcription. Through machine learning, SYNM, TGFBR3, and COL10A1 were screened as key genes. Numbers of CD8 T cells, gamma delta T cells, and M1 macrophages were significantly higher, while monocytes and follicular helper-T cells were significantly lower in the treatment group. The downregulated genes, SYNM and TGFBR3, were positively correlated with CD8 T cells and monocytes, but were negatively correlated with gamma delta T cells and M1 macrophages. The upregulated gene, COL10A1, was positively correlated with gamma delta T cells and M1 macrophages, and was negatively correlated with CD8 T cells, monocytes, and follicular helper-T cells.ConclusionsSYNM, TGFBR3, and COL10A1 are diagnostic genes of breast cancer. They affect breast cancer cells by modulating immune-infiltrating cells.