Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Evaluation of plasma-derived extracellular vesicles miRNAs and their connection with hippocampal mRNAs in alcohol use disorder

Alcohol use disorder (AUD) is a common mental illness with high morbidity and disability. The discovery of laboratory biomarkers has progressed slowly, resulting in suboptimal diagnosis and treatment of AUD. This study aimed to identify promising biomarkers, as well as the potential miRNA-mRNA networks associated with AUD pathogenesis. RNA sequencing was performed on plasma-derived small extracellular vesicles (sEVs) from AUD patients and healthy controls (HCs) to harvest miRNAs expression profiles. Machine learning (ML) models were built to screen characteristic miRNAs, whose target mRNAs were analyzed using TargetScan, miRanda and miRDB databases. Gene Expression Omnibus (GEO) datasets (GSE181804 and GSE180722) providing postmortem hippocampal gene expression profiles of AUD subjects were mined. A total of 247 differentially expressed (DE) plasma-derived sEVs miRNAs and 122 DE hippocampal mRNAs were obtained. Then, 22 overlapping sEVs miRNAs with high importance scores were gained by intersecting 5 ML models. As a result, we established a putative sEVs miRNA-hippocampal mRNA network that can effectively distinguish AUD patients from HCs. In conclusion, we proposed 5 AUD-representative sEVs miRNAs (hsa-miR-144-5p, hsa-miR-182-5p, hsa-miR-142-5p, hsa-miR-7-5p, and hsa-miR-15b-5p) that may participate in the pathogenesis of AUD by modulating downstream target hippocampal genes. These findings may provide novel insights into the diagnosis and treatment of AUD.

PPIH acts as a potential predictive biomarker for patients with common solid tumors

BACKGROUND

Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker.

METHODS

We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers.

RESULTS

Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis.

CONCLUSION

Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Special Issue on Differential Gene Expression and Coexpression

The most common approach in transcriptomics (RNA-seq and microarrays) is differential gene expression analysis (DGEA) [...].

UHPLC-QTOF-MS Metabolic Profiling of Marchantia polymorpha and Evaluation of Its Hepatoprotective Activity Using Paracetamol-Induced Liver Injury in Mice

Marchantia species were traditionally used to treat liver failure. Marchantia polymorpha chloroform extract showed a marked hepatoprotective activity in a dose-dependent manner in paracetamol-induced extensive liver damage in mice. At a dose of 500 mg/kg (MP-500), it resulted in a reduction in aspartate transaminase by 49.44%, alanine transaminase by 44.11%, and alkaline phosphatase by 24.4% with significant elevation in total proteins by 58.69% with respect to the diseased group. It showed significant reductions in total bilirubin, total cholesterol, triglycerides, low density lipoprotein (LDL), very LDL, total lipids, and to high density lipoprotein ratio (CH/HDL) by 53.42, 30.14, 35.02, 45.79, 34.74, 41.45, and 49.52%, respectively, together with a 37.69% increase in HDL with respect to the diseased group. It also showed an elevation of superoxide dismutase by 28.09% and in glutathione peroxidase by 81.83% in addition to the reduction of lipid peroxidation by 17.95% as compared to the paracetamol only treated group. This was further supported by histopathological examination that showed normal liver architecture and a normal sinusoidal gap. Metabolic profiling by ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UHPLC-QTOF/MS) led to the tentative identification of 28 compounds belonging to phenols, quinolones, phenylpropanoid, acylaminosugars, terpenoids, lipids, and fatty acids to which the activity was attributed. Four compounds were detected in the negative ionization mode which are neoacrimarine J, marchantin A, chitobiose, and phellodensin F, while the rest were detected in the positive mode. Thus, it can be concluded that this plant could serve as a valuable choice for the treatment of hepatotoxicity that further consolidated its traditional use.

Identification and preliminary validation of synovial tissue-specific genes and their-mediated biological mechanisms in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. It is well known that the formation of positive feedback between synovial hyperplasia and inflammatory infiltration is intimately associated with the occurrence and development of RA. However, the exact mechanisms still remain unknown, making the early diagnosis and therapy of RA difficult. This study was designed to identify prospective diagnostic and therapeutic biomarkers, as well as their-mediated biological mechanisms in RA.

METHODS

Three microarray datasets (GSE36700, GSE77298 and GSE153015) and two RNA-sequencing datasets (GSE89408 and GSE112656) of synovial tissues, as well as three other microarray datasets (GSE101193, GSE134087 and GSE94519) of peripheral blood were downloaded for integrated analysis. The differently expressed genes (DEGs) were identified by "limma" package of R software. Then, weight gene co-expression analysis and gene set enrichment analysis were performed to investigate synovial tissue-specific genes and their-mediated biological mechanisms in RA. The expression of candidate genes and their diagnostic value for RA were verified by quantitative real-time PCR and receiver operating characteristic (ROC) curve, respectively. Relevant biological mechanisms were explored through cell proliferation and colony formation assay. The suggestive anti-RA compounds were discovered by CMap analysis.

RESULTS

We identified a total of 266 DEGs, which were mainly enriched in cellular proliferation and migration, infection and inflammatory immune signaling pathways. Bioinformatics analysis and molecular validation revealed 5 synovial tissue-specific genes, which exhibited excellent diagnostic value for RA. The infiltration level of immune cells in RA synovial tissue was significantly higher than that in control individuals. Moreover, preliminary molecular experiments suggested that these characteristic genes may be responsible for the high proliferation potential of RA fibroblast-like synoviocytes (FLSs). Finally, 8 small molecular compounds with anti-RA potential were obtained.

CONCLUSIONS

We have proposed 5 potential diagnostic and therapeutic biomarkers (CDK1, TTK, HMMR, DLGAP5, and SKA3) in synovial tissues that may contribute to the pathogenesis of RA. These findings may shed light on the early diagnosis and therapy of RA.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Harshening stem cell research and precision medicine: The states of human pluripotent cells stem cell repository diversity, and racial and sex differences in transcriptomes

In vitro investigation on human development, disease modeling, and drug discovery has been empowered by human induced pluripotent stem cell (hiPSC) technologies that form the foundation of precision medicine. Race and sex genetic backgrounds have become a major focus of many diseases modeling and drug response evaluation in the pharmaceutical industry. Here, we gathered data from major stem cell repositories to analyze the diversity with respect to ethnicity, sex, and disease types; and we also analyzed public datasets to unravel transcriptomics differences between samples of different ethnicities and sexes. We found a lack of diversity despite the large sample size of human induced pluripotent stem cells. In the ethnic comparison, the White group made up the majority of the banked hiPSCs. Similarly, for the organ/disease type and sex comparisons, the neural and male hiPSCs accounted for the majority of currently available hiPSCs. Bulk RNA-seq and single-cell transcriptomic analysis coupled with Machine Learning and Network Analysis revealed panels of gene features differently expressed in healthy hiPSCs and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) of different races and sexes. The data highlights the current ethnic and sex inequality in stem cell research and demonstrates the molecular biological diversity of hiPSCs and cardiomyocytes from different races and genders. We postulate that future efforts in stem cell biology, regenerative and precision medicine should be guided towards an inclusive, diverse repository reflecting the prevalence of diseases across racial and ethnic groups and the sexes, important for both common and rare disease modeling, drug screening, and cell therapeutics.

Identifying key transcription factors and immune infiltration in non-small-cell lung cancer using weighted correlation network and Cox regression analyses

Introduction

Lung cancer is one of the most common cancers and a significant cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases. Therefore, it is crucial to identify effective diagnostic and therapeutic methods. In addition, transcription factors are essential for eukaryotic cells to regulate their gene expression, and aberrant expression transcription factors are an important step in the process of oncogenesis in NSCLC.

Methods

Differentially expressed transcription factors between NSCLC and normal tissues by analyzing mRNA profiling from The Cancer Genome Atlas (TCGA) database program were identified. Weighted correlation network analysis (WGCNA) and line plot of least absolute shrinkage and selection operator (LASSO) were performed to find prognosis-related transcription factors. The cellular functions of transcription factors were performed by 5-ethynyl-2'-deoxyuridine (EdU) assay, wound healing assay, cell invasion assay in lung cancer cells.

Results

We identified 725 differentially expressed transcription factors between NSCLC and normal tissues. Three highly related modules for survival were discovered, and transcription factors highly associated with survival were obtained by using WGCNA. Then line plot of LASSO was applied to screen transcription factors related to prognosis and build a prognostic model. Consequently, SETDB2, SNAI3, SCML4, and ZNF540 were identified as prognosis-related transcription factors and validated in multiple databases. The low expression of these hub genes in NSCLC was associated with poor prognosis. The deletions of both SETDB2 and SNAI3 were found to promote proliferation, invasion, and stemness in lung cancer cells. Furthermore, there were significant differences in the proportions of 22 immune cells between the high- and low-score groups.

Discussion

Therefore, our study identified the transcription factors involved in regulating NSCLC, and we constructed a panel for the prediction of prognosis and immune infiltration to inform the clinical application of transcription factor analysis in the prevention and treatment of NSCLC.

Network analysis of liver cancer: a system biology approach

Aim

Determining critical dysregulated proteins in liver cancer was the main aim of this study.

Background

Liver cancer is a common health problem characterized by difficulties in early diagnosis and rapid progression. Due to the lack of targeted drugs and the other features of the disease, the survival rate for patients is extremely low.

Methods

The related dysregulated proteins for liver cancer were retrieved from the STRING database. The queried proteins were included in a network by Cytoscape software, and the central nodes of the network were enriched via gene ontology.

Results

Among 11 introduced central nodes (GAPDH, TP53, EGFR, MYC, INS, ALB, IL6, AKT1, VEGFA, CDH1, and HRAS), HRAS and AKT1 were highlighted as critical dysregulated proteins which can be considered as possible biomarkers.

Conclusion

Analysis revealed that AKT1, HRAS and the related biochemical pathways (especially "HIF-1 signaling pathway") are the possible diagnostic and therapeutic agents of liver cancer.

Bioinformatics analysis of prognostic value and immunological role of MeCP2 in pan-cancer

Methyl-CpG-binding protein 2(MeCP2) is an important epigenetic regulatory factor that promotes many tumor developments, such as liver cancer, breast cancer, and colorectal cancer. So far, no pan-cancer analysis has been reported. Therefore, this study aims to explore pan-cancer's prognostic value, immune infiltration pattern, and biological function. We used bioinformatics methods to analyze the expression and prognostic significance of MeCP2, and the relationship between MeCP2 and clinicopathological parameters, genetic variation, methylation, phosphorylation, immune cell infiltration, and biological function in pan-cancer from using a public database. The results showed that expression of MeCP2 was up-regulated in 8 cancers and down-regulated in 2 cancers, which was remarkably correlated with the prognosis, pathological stage, grade and subtype of cancers. The promoter methylation level of MeCP2 DNA was decreased in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), liver hepatocellular carcinoma (LIHC), prostate adenocarcinoma (PRAD), uterine corpus endometrial carcinoma (UCEC), testicular germ cell tumors (TGCT), and stomach adenocarcinoma (STAD);decreased phosphorylation of S25, S90, S92, S241, S286, S325 and S435 was found in MeCP2, such as UCEC, lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), colon adenocarcinoma (COAD), and kidney renal clear cell carcinoma (KIRC). Furthermore, MeCP2 expression was significantly associated with multiple immunomodulators and immune cell infiltration levels across most tumors. Therefore, our pan-cancer explored the prognostic markers and immunotherapeutic value of MeCP2 in different cancers.

Characterisation of key biomarkers in diabetic ulcers via systems bioinformatics

Diabetic ulcers (DUs) are characterised by a high incidence and disability rate. However, its pathogenesis remains elusive. Thus, a deep understanding of the underlying mechanisms for the pathogenesis of DUs has vital implications. The weighted gene co-expression network analysis was performed on the main data from the Gene Expression Omnibus database. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were adopted to analyse the potential biological function of the most relevant module. Furthermore, we utilised CytoHubba and protein-protein interaction network to identify the hub genes. Finally, the hub genes were validated by animal experiments in diabetic ulcer mice models. The expression of genes from the turquoise module was found to be strongly related to DUs. GO terms, KEGG analysis showed that biological functions are closely related to immune response. The hub genes included IFI35, IFIT2, MX2, OASL, RSAD2, and XAF1, which were higher in wounds of DUs mice than that in normal lesions. Additionally, we also demonstrated that the expression of hub genes was correlated with the immune response using immune checkpoint, immune cell infiltration, and immune scores. These data suggests that IFI35, IFIT2, MX2, OASL, RSAD2, and XAF1 are crucial for DUs.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Comprehensive Genetic Analysis of Tuberculosis and Identification of Candidate Biomarkers

Purpose: The purpose of this study is to use the data in the GEO database to analyze, screen biomarkers that can diagnose tuberculosis, and verification of candidate biomarkers.

Materials and methods: GSE158767 dataset were used to process WGCNA analysis, differential gene analysis, Gene ontology and KEGG analysis, protein-protein network analysis and hub genes analysis. Based on our previous study, the intersect between WGCNA and differential gene analysis could be used as candidate biomarkers. Then, the enzyme-linked immunosorbent assay was used to validate candidate biomarkers, and receiver operating characteristic was used to assess diagnose ability of candidate biomarkers.

Results: A total of 412 differential genes were screened. And we obtained 105 overlapping genes between DEGs and WGCNA. GO and KEGG analysis showed that most of the differential genes were significantly enriched in innate immunity. A total of 15 hub genes were screened, and four of them were verified by Enzyme-linked immunosorbent assay. CCL5 performed well in distinguishing the healthy group from the TB group (AUC = 0.723). And CCL19 performed well in distinguishing the TB group from the ORD groups (AUC = 0.811).

Conclusion: CCL19, C1Qb, CCL5 and HLA-DMB may play important role in tuberculosis, which indicated four genes may become effective biomarkers and could be conveniently used to facilitate the individual tuberculosis diagnosis in Chinese people.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005−2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.