Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer

Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is involved in the progression of BC. This study aimed to developed a novel diagnostic model based on mitochondria-related genes (MRGs) for BC patients using Machine Learning. In this study, we analyzed GSE13507 datasets and identified 752 DE-MRGs in BC specimens. Functional enrichment analysis uncovered the significant roles of 752 DE-MRGs in key processes such as cellular and organ development, as well as gene regulation. The analysis revealed the crucial functions of these genes in transcriptional regulation and protein-DNA interactions. Then, we performed LASSO and SVM-RFE, and identified four critical diagnostic genes including GLRX2, NMT1, OXSM and TRAF3IP3. Based on the above four genes, we developed a novel diagnostic model whose diagnostic value was confirmed in GSE13507, GSE3167 and GSE37816 datasets. Moreover, we reported the expressing pattern of GLRX2, NMT1, OXSM and TRAF3IP3 in BC samples. Immune cell infiltration analysis revealed that the four genes were associated with several immune cells. Finally, we performed RT-PCR and confirmed NMT1 was highly expressed in BC cells. Functional experiments revealed that knockdown of NMT1 suppressed the proliferation of BC cells. Overall, we have formulated a diagnostic potential that offered a comprehensive framework for delving into the underlying mechanisms of BC. Before proceeding with clinical implementation, it is essential to undertake further investigative efforts to validate its diagnostic effectiveness in BC patients.

Correlation of caecal microbiome endotoxins genes and intestinal immune cells in Eimeria tenella infection based on bioinformatics

Introduction

The infection with Eimeria tenella (ET) can elicit expression of various intestinal immune cells, incite inflammation, disrupt intestinal homeostasis, and facilitate co-infection with diverse bacteria. However, the reciprocal interaction between intestinal immune cells and intestinal flora in the progression of ET-infection remains unclear.

Objective

The aim of this study was to investigate the correlation between cecal microbial endotoxin (CME)-related genes and intestinal immunity in ET-infection, with subsequent identification of hub potential biomarker and immunotherapy target.

Methods

Differential expression genes (DEGs) within ET-infection and hub genes related to CME were identified through GSE39602 dataset based on bioinformatic methods and Protein-protein interaction (PPI) network analysis. Moreover, immune infiltration was analyzed by CIBERSORT method. Subsequently, comprehensive functional enrichment analyses employing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis along with Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were performed.

Results

A total of 1089 DEGs and 25 hub genes were identified and CXCR4 was ultimately identified as a essential CME related potential biomarker and immunotherapy target in the ET-infection. Furthermore, activated natural killer cells, M0 macrophages, M2 macrophages, and T regulatory cells were identified as expressed intestinal immune cells. The functional enrichment analysis revealed that both DEGs and hub genes were significantly enriched in immune-related signaling pathways.

Conclusion

CXCR4 was identified as a pivotal CME-related potential biomarker and immunotherapy target for expression of intestinal immune cells during ET-infection. These findings have significant implications in elucidating the intricate interplay among ET-infection, CME, and intestinal immunity.

Investigating the mechanism of Sinisan formula in depression treatment: a comprehensive analysis using GEO datasets, network pharmacology, and molecular docking

The herbal formula Sinisan (SNS) is a commonly used treatment for depression; however, its mechanism of action remains unclear. This article uses a combination of the GEO database, network pharmacology and molecular docking technologies to investigate the mechanism of action of SNS. The aim is to provide new insights and methods for future depression treatments. The study aims to extract effective compounds and targets for the treatment of depression from the T CMSP database. Relevant targets were searched using the GEO, Disgenet, Drugbank, PharmGKB and T T D databases, followed by screening of core targets. In addition, GO and KEGG pathway enrichment analyses were performed to explore potential pathways for the treatment of depression. Molecular docking was used to evaluate the potential targets and compounds and to identify the optimal core protein-compound complex. Molecular dynamics was used to further investigate the dynamic variability and stability of the complex. The study identified 118 active SNS components and 208 corresponding targets. Topological analysis of P P I networks identified 11 core targets. GO and KEGG pathway enrichment analyses revealed that the mechanism of action for depression involves genes associated with inflammation, apoptosis, oxidative stress, and the MAP K3 and P I3K-Akt signalling pathways. Molecular docking and dynamics simulations showed a strong binding affinity between these compounds and the screened targets, indicating promising biological activity. The present study investigated the active components, targets and pathways of SNS in the treatment of depression. Through a preliminary investigation, key signalling pathways and compounds were identified. These findings provide new directions and ideas for future research on the therapeutic mechanism of SNS and its clinical application in the treatment of depression.Communicated by Ramaswamy H. Sarma.

Differential expression of miR-140-3p and its potential role during the development of the acute coronary syndrome

BACKGROUND

Acute coronary syndrome (ACS) is a category of cardiovascular disease with a high fatality rate.

AIMS

We searched the differential expressed miRNAs (DEmiRNAs) in ACS based on bioinformatic analysis and investigated the diagnostic value of plasma miR-140-3p in patients with ACS and its potential functional role in ACS.

METHODS

The miRNAs (GSE94605, GSE49823, and GSE185729) microarray datasets of ACS were downloaded from the GEO datasets. After integrating the miRNA and mRNA interaction, a protein-protein interaction (PPI) network was constructed with 36 overlapped target mRNAs using STRING database. The plasma levels of miR-140-3p were detected by RT-qPCR, and its clinical diagnostic value was evaluated using the ROC curve. The potential effects of the miR-140-3p/RHOA axis in ACS were explored using human coronary endothelial cells (HCAECs).

RESULTS

After overlapping the GEO datasets, miR-140-3p was identified in the microarray datasets of ACS. The plasma miR-140-3p expression levels were highly expressed in ACS patients than in healthy control and had diagnostic significance. The target mRNAs of miR-140-3p were predicted using TargetScan, miRWalk, TarBase, and miRDB databases. The PPI network identified ten hub genes. miR-140-3p could decrease the HCAECs' cell viability, while RHOA reversed the inhibition effect of miR-140-3p.

CONCLUSIONS

The plasma expression of miR-140-3p was upregulated in ACS patients. miR-140-3p could decrease the HCAECs' cell viability, while RHOA reversed the inhibition effect of miR-140-3p. The miR-140-3p may be a potential diagnostic biomarker for the early detection of ACS.

The necroptosis signature and molecular mechanism of lung squamous cell carcinoma

BACKGROUND

Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers.

METHODS

The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA. A necroptosis signature was constructed by survival analysis, and its correlation with patient prognosis and clinical features was evaluated. The molecular characteristics and drug response associated with the necroptosis signature were also identified. The drug candidates were then validated at the cellular level.

RESULTS

The TCGA_LUSC dataset included 51 normal samples and 502 LUSC samples. The GSE73403 dataset included 69 samples. 159 genes involved in necroptosis pathways were acquired from the KEGG database, of which most showed significant differences between two groups in terms of genomic, transcriptional and methylation alterations. In particular, CHMP4C, IL1B, JAK1, PYGB and TNFRSF10B were significantly associated with the survival (p < 0.05) and were used to construct the necroptosis signature, which showed significant correlation with patient prognosis and clinical features in univariate and multivariate analyses (p < 0.05). Furthermore, CHMP4C, IL1B, JAK1 and PYGB were identified as potential targets of trametinib, selumetinib, SCH772984, PD 325901 and dasatinib. Finally, knockdown of these genes in LUSC cells increased chemosensitivity to those drugs.

CONCLUSION

We identified a necroptosis signature in LUSC that can predict prognosis and identify patients who can benefit from targeted therapies.

Transcriptomic profiling of hepatic tissues for drug metabolism genes in nonalcoholic fatty liver disease: A study of human and animals

Background

Drug metabolism genes are involved in the in vivo metabolic processing of drugs. In previous research, we found that a high-fat diet affected the transcript levels of mouse hepatic genes responsible for drug metabolism.

Aims

Our research intends to discover the drug metabolism genes that are dysregulated at the transcriptome level in nonalcoholic fatty liver disease (NAFLD).

Methods

We analyzed the transcriptome for drug metabolism genes of 35 human liver tissues obtained during laparoscopic cholecystectomy. Additionally, we imported transcriptome data from mice fed a high-fat diet in previous research and two open-access Gene Expression Omnibus (GEO) datasets (GSE63067 and GSE89632). Then, using quantitative real-time polymerase chain reaction (qRT-PCR), we cross-linked the differentially expressed genes (DEGs) in clinical and animal samples and validated the common genes.

Results

In this study, we identified 35 DEGs, of which 33 were up-regulated and two were down-regulated. Moreover, we found 71 DEGs (39 up- and 32 down-regulated), 276 DEGs (157 up- and 119 down-regulated), and 158 DEGs (117 up- and 41 down-regulated) in the GSE63067, GSE89632, and high-fat diet mice, respectively. Of the 35 DEGs, nine co-regulated DEGs were found in the Venn diagram (CYP20A1, CYP2U1, SLC9A6, SLC26A6, SLC31A1, SLC46A1, SLC46A3, SULT1B1, and UGT2A3).

Conclusion

Nine significant drug metabolism genes were identified in NAFLD. Future research should investigate the impacts of these genes on drug dose adjustment in patients with NAFLD.

Clinical Trial Registration

http://www.chictr.org.cn, identifier ChiCTR2100041714.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

The Integrated Analysis Identifies Three Critical Genes as Novel Diagnostic Biomarkers Involved in Immune Infiltration in Atherosclerosis

Atherosclerosis (AS), a chronic inflammatory disease of the blood vessels, is the primary cause of cardiovascular disease, the leading cause of death worldwide. This study aimed to identify possible diagnostic markers for AS and determine their correlation with the infiltration of immune cells in AS. In total, 10 serum samples from AS patients and 10 samples from healthy subjects were collected. The original gene expression profiles of GSE43292 and GSE57691 were downloaded from the Gene Expression Omnibus database. Least absolute shrinkage and selection operator regression model and support vector machine recursive feature elimination analyses were carried out to identify candidate markers. The diagnostic values of the identified biomarkers were determined using receiver operating characteristic assays. The compositional patterns of the 22 types of immune cell fraction in AS were estimated using CIBERSORT. RT-PCR was performed to further determine the expression of the critical genes. This study identified 17 differentially expressed genes (DEGs) in AS samples. The identified DEGs were mainly involved in non-small cell lung carcinoma, pulmonary fibrosis, polycystic ovary syndrome, glucose intolerance, and T-cell leukemia. FHL5, IBSP, and SCRG1 have been identified as the diagnostic genes in AS. The expression of SCRG1 and FHL5 was distinctly downregulated in AS samples, and the expression of IBSP was distinctly upregulated in AS samples, which was further confirmed using our cohort by RT-PCR. Moreover, immune assays revealed that FHL5, IBSP, and SCRG1 were associated with several immune cells, such as CD8 T cells, naïve B cells, macrophage M0, activated memory CD4 T cells, and activated NK cells. Overall, future investigations into the occurrence and molecular mechanisms of AS may benefit from using the genes FHL5, IBSP, and SCRG1 as diagnostic markers for the condition.

The Highly Expressed IFIT1 in Nasopharyngeal Carcinoma Enhances Proliferation, Migration, and Invasion of Nasopharyngeal Carcinoma Cells

In this study, we aimed to identify potential targets modulating the progression of nasopharyngeal carcinoma (NPC) using integrated bioinformatics analysis and functional assays. Differentially expressed genes (DEGs) between NPC and normal tissues samples were obtained from publicly availably microarray datasets (GSE68799, GSE34573, and GSE53819) in the Gene Expression Omnibus (GEO) database. The bioinformatics analysis identified 49 common DEGs from three GEO datasets, which were mainly enriched in cytokine/chemokine pathways and extracellular matrix organization pathway. Further protein-protein interaction network analysis identified 11 hub genes from the 49 DEGs. The 11 hub genes were significantly up-regulated in the NPC tissues when compared to normal tissues by analyzing the Oncomine database. The 8 hub genes including COL5A1, COL7A1, COL22A1, CXCL11, IFI44L, IFIT1, RSAD2, and USP18 were significantly up-regulated in the NPC tissues when compared to normal tissues by using the Oncomine database. Further validation studies showed that IFIT1 was up-regulated in the NPC cells. Knockdown of IFI1T1 suppressed the proliferation, migration, and invasion of NPC cells; while IFIT1 overexpression promoted the proliferation, migration, and invasion of NPC cells. In conclusion, a total of 49 DEGs and 11 hub genes in NPC using the integrated bioinformatics analysis. IFIT1 was up-regulated in the NPC cells lines, and IFIT1 may act as an oncogene by promoting NPC cell proliferation, migration, and invasion.

Analyzing large scale gene expression data in colorectal cancer reveals important clues; CLCA1 and SELENBP1 downregulated in CRC not in normal and not in adenoma

Early detection of colorectal cancer (CRC) increases the chances of survival and reduces the therapeutic problems and costs of treatment. Since molecular biomarkers can help us diagnose colorectal cancer early, we need to identify novel gene for predicting the early stages of tumorigenesis. Here, we integrated five independent CRC gene expression datasets derived from expression profiling by array comparing CRC with normal samples in: GSE21510, GSE4107, GSE25071, GSE15781 dataset, and GSE8671 dataset, including 64 samples from 32 patients comparing 32 colonic normal mucosa with 32 colorectal adenoma. To detect genes that expressed differentially in experimental circumstances of these datasets, we used web tool of GEO2R to compare groups of samples in the GEO data series. Furthermore, we constructed the protein-protein interactions network by STRING database for mostly downregulated genes and the expression of their members in PPI network were studied into five datasets separately. Also, the level of expression of selected biomarker genes in different stages of CRC compared to normal was studied. Our data revealed 17 common downregulated genes (average fold change (FC) in five tests ≥6) in CRC in comparison with normal (Test 1 to Test 4) and in adenoma compared with normal (Test 5). Studying of gene expression of PPI network members of these downregulated genes led to identifying of CLCA1, SELENBP1, CWC25, ACOT11, GUCY2C and ALDH1A1 as suppressor genes and PTGS2, PROCR, MOCS3 and NFS1 as oncogenes which respectively downregulated and upregulated in CRC. Since decreasing of gene expression was seen in CRC comparing with normal and due to no different expression seen for these 10 genes in adenoma, they, especially CLCA1 and SELENBP1, could be considered as biomarkers for early detection of CRC. Before using these signature genes in the clinic; however, further validations are required.

A novel prognostic 6-gene signature for osteoporosis

INTRODUCTION

The incidence of osteoporosis (OP) keeps increasing due to global aging of the population. Therefore, identifying the diagnostic and prognostic biomarkers of OP is of great significance.

METHODS

mRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815). Gene modules most significantly related to OP were revealed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) between OP and normal samples in training set were identified using limma R package. Thereafter, above two gene sets were intersected to obtain the genes potentially related to OP. Protein-protein interaction (PPI) pairs were screened by STRING database and visualized using Cytoscape, while the plug-in cytoHubba was used to screen hub genes by determining their topological parameters. Afterwards, a diagnostic model was constructed using those hub genes, whose creditability was further evaluated by testing sets.

RESULTS

The results of WGCNA analysis found the Black module was most significantly related to OP, which included altogether 1286 genes. Meanwhile, 2771 DEGs were discovered between OP patients and the normal controls. After taking the intersection, 479 genes were identified potentially correlated with the development of OP. Subsequently, six hub genes were discovered through PPI network construction and node topological analysis. Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups.

CONCLUSION

Our current study constructed a six hub genes-based diagnostic model for OP. Our findings may shed some light on the research of the early diagnosis for OP and had certain practical significance.

Low expression of CDHR1 is an independent unfavorable prognostic factor in glioma

Background: Analysis of the differentially expressed genes between lower grade glioma (LGG) and glioblastoma (GBM) will identify genes involved in a more aggressive phenotype of glioma. Methods: Differentially expressed genes between GBM and LGG were identified using published datasets. Kaplan-Meier estimator was used to determine the overall survival of different groups of glioma patients. The biological functions of CDHR1 in glioma were tested using CCK-8 and trans-well assays. Results: CCDC109B, CD58, CLIC1, EFEMP2, EMP3, LAMC1, LGALS1, PDLIM1 and TNFRSF1A were over-expressed, while, CDHR1 was down-regulated in GBM in The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE4412 and GSE43378 datasets. Compared with normal brain tissues, CDHR1 was down-regulated in glioma tissues. And low expression of CDHR1 was an unfavorable prognostic factor in glioma. Moreover, CDHR1 was lowly expressed in mesenchymal GBM subtype and lower expression of CDHR1 was associated with the worse clinical prognosis of GBM. Furthermore, CDHR1 was down-regulated in astrocytoma LGG subtype and low expression of CDHR1 was a bad prognosis of LGG. CDHR1 expression levels were also associated with IDH mutation. IDH mutant LGG or GBM patients were with higher CDHR1 expression. High expression of CDHR1 was a favorable prognosis in IDH mutant or IDH wild type LGG patients. CHDR1 expression was associated with MGMT methylation and CDHR1 was down-regulated in chemotherapy un-responsive LGG patients. CDHR1 was an independent prognostic factor and negatively associated with EMP3 expression. Glioma patients with low CDHR1 and high EMP3 expression had worse clinical outcomes. At last, we showed that over-expression of CDHR1 could inhibit glioma cell growth and invasion. Conclusion: Low expression of CDHR1 was an independent unfavorable prognostic factor in glioma.

The role of PI3K signaling pathway and its associated genes in papillary thyroid cancer

BACKGROUND

One of the well-differentiated types of thyroid cancer is papillary thyroid cancer (PTC), often developed by genetic mutations and radiation.

METHODS

In this study, the public NCBI-GEO database was systematically searched. The eligible datasets were the targets for biomarker discovery associated with PI3K signaling pathway.

RESULTS

Only two datasets were suitable and passed the inclusion criteria. The meta-analysis outcomes revealed eleven upregulation and thirteen downregulation genes differentially expressed between PTC and healthy tissues. Moreover, the outcomes for survival and disease-free rates for each gene were illustrated.

CONCLUSIONS

The present research suggests a panel signature of 24 gene biomarkers in diagnosing the PTC.

Integrative Analysis of the Doxorubicin-Associated LncRNA-mRNA Network Identifies Chemoresistance-Associated lnc-TRDMT1-5 as a Biomarker of Breast Cancer Progression

Increasing evidence has revealed close relationships between long non-coding RNAs (lncRNAs) and chemoresistance in multiple types of tumors; however, functional lncRNAs in breast cancer (BC) have not been completely identified. In this study, we aimed to identify novel lncRNAs that might play critical roles in doxorubicn resistance, which could reveal potential biomarkers of BC. Using a BC dataset (GSE81971), we identified 452 lncRNAs that were upregulated and 659 that were downregulated; furthermore, there were 1896 differentially expressed mRNAs, of which 1137 were upregulated and 758 were downregulated in MCF-7/ADR cells compared with the expression in MCF-7 cells. We constructed an lncRNA–mRNA network by integrating probe reannotation and regulatory interactions. To elucidate the key lncRNAs in BC, we further analyzed dysregulated lncRNA–mRNA crosstalk, and six candidate lncRNAs (lnc-TRDMT1-5, ZNF667-AS1, lnc-MPPE1-13, DSCAM-AS1:5, DSCAM-AS1:2, and lnc-CFI-3) were identified. Notably, the expression level of lnc-TRDMT1-5 was significantly upregulated in resistant cells compared with sensitive cells, and its levels were increased in BC tissues compared with adjacent tissues. Levels were positively associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) expression levels. High expression of lnc-TRDMT1-5 predicted poor prognosis in ER-positve and HER2-positive BC patients, especially in patients with chemoresistance. Bioinformatic and functional analysis revealed that lnc-TRDMT1-5 was involved in many crucial pathways in cancer, such as the PI3K/AKT and Wnt signaling pathways. Subcellular localization predicted that lnc-TRDMT1-5 was located in the cytoplasm, and the lncRNA–miRNA–mRNA network showed that lnc-TRDMT1-5 might serve as a regulator in BC. Here, our results demonstrated a dysregulated lncRNA–mRNA network that might provide new treatment strategies for chemoresistant BC, and the results identified a new lncRNA, lnc-TRDMT1-5, with oncogenic and prognostic functions in human BC.

Long Non-coding RNA Expression Profiling in Biopsy to Identify Renal Allograft at Risk of Chronic Damage and Future Graft Loss

The loss of allograft from chronic damage is still the major risk that renal transplant recipients face today. Biomarkers for early detection of chronic damage are needed to improve the long-term graft survival. This study aimed to identify long non-coding RNA (lncRNA) biomarkers associated with chronic damage and graft loss after renal transplantation. Gene Expression Omnibus (GEO) datasets including GSE57387 (n = 101), GSE21374 (n = 282), and GSE25902 (n = 24) from three high-quality studies were analyzed. By repurposing the publicly available array-based data coupled with Affymetrix Human Exon 1.0 ST and Human U133 Plus 2.0 arrays, we obtained expression profiles of 11323 and 3383 lncRNAs in biopsies after renal transplantation, respectively. The logistic regression model and Cox regression model were applied to identify lncRNAs associated with chronic damage and graft survival. High AC093673.5 expression was identified as significantly associated with the three endpoints including chronic damage, progressive chronic histological damage, and graft failure across these three datasets. A six-lncRNA signature was created to predict renal allograft at risk of chronic damage with a high predictive ability (AUC = 0.94). Gene set enrichment analysis (GSEA) indicated that our lncRNA signature was related with allograft rejection and immunity. Our study highlights the importance of lncRNAs in chronic graft damage and allograft loss, supporting their potential role as prognosis biomarkers.