Bioinformatics Analysis Explores Potential Hub Genes in Nonalcoholic Fatty Liver Disease

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma

BACKGROUND

Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis.

METHODS

Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy.

RESULTS

We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility.

CONCLUSION

To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Exploring Key Genes with Diagnostic Value for Nonalcoholic Steatohepatitis Based on Bioinformatics Analysis

We aimed to screen specific genes in liver tissue samples of patients with nonalcoholic steatohepatitis (NASH) with clinical diagnostic value based on bioinformatics analysis. The datasets of liver tissue samples from healthy individuals and NASH patients were retrieved for consistency cluster analysis to obtain the NASH sample typing, followed by verification of the diagnostic value of sample genotyping-specific genes. All samples were subjected to logistic regression analysis, followed by the establishment of the risk model, and then, the diagnostic value was determined by receiver operating characteristic curve analysis. NASH samples could be divided into cluster 1, cluster 2, and cluster 3, which could predict the nonalcoholic fatty liver disease activity score of patients. A total of 162 sample genotyping-specific genes were extracted from patient clinical parameters, and the top 20 core genes in the protein interaction network were obtained for logistic regression analysis. Five sample genotyping-specific genes (WD repeat and HMG-box DNA-binding protein 1 [WDHD1], GINS complex subunit 2 [GINS2], replication factor C subunit 3 (RFC3), secreted phosphoprotein 1 [SPP1], and spleen tyrosine kinase [SYK]) were extracted to construct the risk models with high diagnostic value in NASH. Compared with the low-risk group, the high-risk group of the model showed increased lipoproduction and decreased lipolysis and lipid β oxidation. The risk models based on WDHD1, GINS2, RFC3, SPP1, and SYK have high diagnostic value in NASH, and this risk model is closely related to lipid metabolism pathways.

Investigating the effects of long-term Aroclor 1260 exposure on fatty liver disease in a diet-induced obesity mouse model

Introduction

Polychlorinated biphenyls (PCBs) are persistent environmental toxicants that have been implicated in numerous health disorders including liver diseases such as non-alcoholic fatty liver disease (NAFLD). Toxicant-associated NAFLD, also known as toxicant-associated fatty liver disease (TAFLD), consists of a spectrum of disorders ranging from steatosis and steatohepatitis to fibrosis and hepatocellular carcinoma. Previously, our group demonstrated that 12-week exposure to the PCB mixture, Aroclor 1260, exacerbated steatohepatitis in high-fat diet (HFD)-fed mice; however, the longer-term effects of PCBs on TAFLD remain to be elucidated. This study aims to examine the longer-term effects of Aroclor 1260 (>30 weeks) in a diet-induced obesity model to better understand how duration of exposure can impact TAFLD.

Methods

Male C57BL/6 mice were exposed to Aroclor 1260 (20 mg/kg) or vehicle control by oral gavage at the beginning of the study period and fed either a low-fat diet (LFD) or HFD throughout the study period.

Results

Aroclor 1260 exposure (>30 weeks) led to steatohepatitis only in LFD-fed mice. Several Aroclor 1260 exposed LFD-fed mice also developed hepatocellular carcinoma (25%), which was absent in HFD-fed mice. The LFD+Aroclor1260 group also exhibited decreased hepatic Cyp7a1 expression and increased pro-fibrotic Acta2 expression. In contrast, longer term Aroclor 1260 exposure in conjunction with HFD did not exacerbate steatosis or inflammatory responses beyond those observed with HFD alone. Further, hepatic xenobiotic receptor activation by Aroclor 1260 was absent at 31 weeks post exposure, suggesting PCB redistribution to the adipose and other extra-hepatic tissues with time.

Discussion

Overall, the results demonstrated that longer-term PCB exposure worsened TAFLD outcomes independent of HFD feeding and suggests altered energy metabolism as a potential mechanism fueling PCB mediated toxicity without dietary insult. Additional research exploring mechanisms for these longer-term PCB mediated toxicity in TAFLD is warranted.

Microanatomy of the metabolic associated fatty liver disease (MAFLD) by single-cell transcriptomics

BACKGROUND

Metabolic-associated fatty liver disease (MAFLD) is a major cause of liver disease worldwide and comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to identify and treat patients at risk. However, the aetiology and mechanisms remain largely unknown, warranting further analysis.

METHODS

We first identified differential genes in NASH by single-cell analysis of the GSE129516 dataset and conducted expression profiling data analysis of the GSE184019 dataset from the Gene Expression Omnibus (GEO) database. Then single-cell trajectory reconstruction and analysis, immune gene score, cellular communication, key gene screening, functional enrichment analysis, and immune microenvironment analysis were carried out. Finally, cell experiments were performed to verify the role of key genes in NASH.

RESULTS

We conducted transcriptome profiling of 30,038 single cells, including hepatocytes and non-hepatocytes from normal and steatosis adult mouse livers. Comparative analysis of hepatocytes and non-hepatocytes revealed pronounced heterogeneity as non-hepatocytes acted as major cell-communication hubs. The results showed that Hspa1b, Tfrc, Hmox1 and Map4k4 could effectively distinguish NASH tissues from normal samples. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in NASH were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed significant differences in M2 macrophage distribution between healthy and metabolic-associated fatty liver samples.

CONCLUSIONS

Our results suggest that Hspa1b, Tfrc, Hmox1 and Map4k4 have huge prospects as diagnostic and prognostic biomarkers for NASH and may be potential therapeutic targets for NASH.

Could Long Non-Coding RNA MEG3 and PTENP1 Interact with miR-21 in the Pathogenesis of Non-Alcoholic Fatty Liver Disease?

NAFLD is the most common cause of chronic liver disease worldwide. The miRNAs and lncRNAs are important endogenous ncRNAs families that can regulate molecular mechanisms. The aim of this study was to analyze the miRNA and lncRNA expression profiles in serum samples of NAFLD patients with different types of hepatosteatosis compared to healthy controls by the qPCR method. A total of180 NAFLD patients and 60 healthy controls were included. miRCURY LNA miRNA miRNome PCR human panel I + II kit and LncProfiler qPCR Array Kit were used to detect miRNA and lncRNA expression, respectively. DIANA miRPath and DIANA-lncBase web servers were used for interaction analysis. As a result, 75 miRNA and 24 lncRNA expression changes were determined. For miRNAs and lncRNAs, 30 and 5 were downregulated and 45 and 19 were upregulated, respectively. hsa-miR-21 was upregulated 2-fold whereas miR-197 was downregulated 0.25-fold. Among lncRNAs, NEAT1 was upregulated 2.9-fold while lncRNA MEG3 was downregulated 0.41-fold. A weak correlation was found between hsa-miR-122 and lncRNA MALAT1. As a conclusion, it is clear that lncRNA-miRNA interaction is involved in the molecular mechanisms of the emergence of NAFLD. The lncRNAs MEG3 and PTENP1 interacted with hsa-miR-21. It was thought that this interaction should be investigated as a biomarker for the development of NAFLD.

Identification and Evaluation of Hub Long Noncoding RNAs and mRNAs in High Fat Diet Induced Liver Steatosis

Nonalcoholic fatty liver disease (NAFLD) is considered the most prevalent chronic liver disease, but the understanding of the mechanism of NAFLD is still limited. The aim of our study was to explore hub lncRNAs and mRNAs and pathological processes in high-fat diet (HFD)-induced and lycopene-intervened liver steatosis. We analyzed the gene profiles in the GSE146627 dataset from the Gene Expression Omnibus (GEO) database to identify differentially expressed lncRNAs and mRNAs, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized for functional enrichment analysis. We found that the turquoise, blue, brown, yellow, green, and black modules were significantly correlated with NAFLD. Functional enrichment analysis revealed that some hub lncRNAs (Smarca2, Tacc1, Flywch1, and Mef2c) might be involved in the regulation of the inflammatory and metabolic pathways (such as TNF signaling, metabolic, mTOR signaling, MAPK signaling, and p53 signaling pathways) in NAFLD. The establishment of an NAFLD mouse model confirmed that lycopene supply attenuated hepatic steatosis in HFD-induced NAFLD. Our analysis revealed that the inflammatory and metabolic pathways may be crucially involved in the pathogenesis of NAFLD, and hub lncRNAs provide novel biomarkers, therapeutic ideas, and targets for NAFLD. Moreover, lycopene has the potential to be a phytochemical for the prevention of HFD-induced liver steatosis.

Identification of potential biomarkers in Barrett's esophagus derived esophageal adenocarcinoma

Almost 50% of esophageal adenocarcinoma (EAC) patients progressed from Barrett's esophagus (BE). EAC is often diagnosed at late stages and is related to dismal prognosis. However, there are still no effective methods for stratification and therapy in BE and EAC. Two public datasets (GSE26886 and GSE37200) were analyzed to identify differentially expressed genes (DEGs) between BE and EAC. Then, a series of bioinformatics analyses were performed to explore potential biomarkers associated with BE-EAC. 27 up- and 104 down-regulated genes were observed between GSE26886 and GSE37200. The GO and KEGG enrichment analysis indicated that the DEGs were highly involved in tumorigenesis. Subsequently, Weighted Gene Co-Expression Network Analysis (WGCNA) were performed to explore the potential genes related to BE-EAC, which were validated in The Cancer Genome Atlas (TCGA) database, and 5 up-regulated genes (MYO1A, ACE2, COL1A1, LGALS4, and ADRA2A) and 3 down-regulated genes (AADAC, RAB27A, and P2RY14) were found in EAC. Meanwhile, ADRA2A and AADAC could contribute to EAC pathogenesis and progression. MYO1A, ACE2, COL1A1, LGALS4, ADRA2A, AADAC, RAB27A, and P2RY14 could be potential novel diagnostic and prognostic biomarkers in BE-EAC.

Comparison of copper concentration between non-alcoholic fatty liver disease patients and normal individuals: A meta-analysis

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Copper metabolism plays an important role in the pathogenesis of NAFLD. However, the relationship between serum/hepatic copper concentration and NAFLD is still debated. A literature search was performed using electronic databases to find publications up to September 2022, where the relationship between serum/hepatic copper or ceruloplasmin concentration and NAFLD was evaluated. Finally, 6 articles with 9 unique outcomes involving 2,607 NAFLD patients and 1,441 non-NAFLD normal individuals were included. The pooled results showed that hepatic copper concentration was significantly decreased in NAFLD patients (SMD = -0.98, 95% CI = [-1.21; -0.74], p < 0.0001), and the sensitivity analysis also confirmed this. Nevertheless, serum copper (SMD = -0.02, 95% CI = [-0.32; 0.28], p = 0.88) and ceruloplasmin (SMD = -0.03, 95% CI = [-0.69; 0.63], p = 0.93) were not associated with NAFLD. This meta-analysis revealed that low hepatic copper concentration was found in NAFLD patients and serum copper and ceruloplasmin were not associated with NAFLD. Larger cohort studies and related trials are needed to further validate the result of this meta-analysis in the future.

Identification of Cuproptosis-Related Genes in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent hepatic pathology worldwide. However, the precise molecular mechanisms for NAFLD are still not sufficiently explained. Recently, a new mode of cell death (cuproptosis) is found. However, the relationship between NAFLD and cuproptosis remains unclear. We analyzed three public datasets (GSE89632, GSE130970, and GSE135251) to identify cuproptosis-related genes stably expressed in NAFLD. Then, we performed a series of bioinformatics analyses to explore the relationship between NAFLD and cuproptosis-related genes. Finally, 6 high-fat diet- (HFD-) induced NAFLD C57BL/6J mouse models were established to carry out transcriptome analysis. The results of gene set variation analysis (GSVA) revealed that the cuproptosis pathway was abnormally activated to a certain degree (p = 0.035 in GSE89632, p = 0.016 in GSE130970, p = 0.22 in GSE135251), and the principal component analysis (PCA) of the cuproptosis-related genes showed that the NAFLD group separated from the control group, with the first two principal components accounting for 58.63%-74.88% of the variation. Among three datasets, two cuproptosis-related genes (DLD and PDHB, p < 0.01 or 0.001) were stably upregulated in NAFLD. Additionally, both DLD (AUC = 0.786-0.856) and PDHB (AUC = 0.771-0.836) had favorable diagnostic properties, and the multivariate logistics regression model further improved the diagnostic properties (AUC = 0.839-0.889). NADH, flavin adenine dinucleotide, and glycine targeted DLD, and pyruvic acid and NADH targeted PDHB in the DrugBank database. The DLD and PDHB were also associated with clinical pathology, especially with steatosis (DLD, p = 0.0013-0.025; PDHB, p = 0.002-0.0026) and NAFLD activity score (DLD, p = 0.004-0.02; PDHB, p = 0.003-0.031). What is more, DLD and PDHB were correlated with stromal score (DLD, R = 0.38, p < 0.001; PDHB, R = 0.31, p < 0.001) and immune score (DLD, R = 0.26, p < 0.001; PDHB, R = 0.27, p < 0.001) in NAFLD. Furthermore, Dld and Pdhb were also significantly upregulated in the NAFLD mouse model. In conclusion, cuproptosis pathways, especially DLD and PDHB, could be potential candidate genes for NAFLD diagnostic and therapeutic options.

Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis

INTRODUCTION

Non-alcoholic steatohepatitis (NASH), an advanced subtype of non-alcoholic fatty liver disease (NAFLD), has becoming the most important aetiology for end-stage liver disease, such as cirrhosis and hepatocellular carcinoma. This study were designed to explore novel genes associated with NASH.

METHODS

Here, five independent Gene Expression Omnibus (GEO) datasets were combined into a single cohort and analyzed using network biology approaches.

RESULTS

11 modules identified by weighted gene co-expression network analysis (WGCNA) showed significant association with the status of NASH. Further characterization of four gene modules of interest demonstrated that molecular pathology of NASH involves the upregulation of hub genes related to immune response, cholesterol and lipid metabolic process, extracellular matrix organization, and the downregulation of hub genes related to cellular amino acid catabolic, respectively. After DEGs enrichment analysis and module preservation analysis, the Turquoise module associated with immune response displayed a remarkably correlation with NASH status. Hub genes with high degree of connectivity in the module, including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN were further verified in clinical samples and mouse model of NASH. Moreover, single-cell RNA-seq analysis showed that those key genes were expressed by distinct immune cells such as microphages, natural killer, dendritic, T and B cells. Finally, the potential transcription factors of Turquoise module were characterized, including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which increased with NASH progression.

DISCUSSION

In conclusion, our integrative analysis will contribute to the understanding of NASH and may enable the development of potential biomarkers for NASH therapy.

Red and Blue Light Affect the Formation of Adventitious Roots of Tea Cuttings (Camellia sinensis) by Regulating Hormone Synthesis and Signal Transduction Pathways of Mature Leaves

Light is an important environmental factor which affects plant growth, through changes of intensity and quality. In this study, monochromatic white (control), red (660 nm), and blue (430 nm) light-emitting diodes (LEDs) were used to treat tea short cuttings. The results showed the most adventitious roots in blue light treated tea cuttings, but the lowest roots in that treated by red light. In order to explore the molecular mechanism of light quality affecting adventitious root formation, we performed full-length transcriptome and metabolome analyses of mature leaves under three light qualities, and then conducted weighted gene co-expression network analysis (WGCNA). Phytohormone analysis showed that Indole-3-carboxylic acid (ICA), Abscisic acid (ABA), ABA-glucosyl ester (ABA-GE), trans-Zeatin (tZ), and Jasmonic acid (JA) contents in mature leaves under blue light were significantly higher than those under white and red light. A crosstalk regulatory network comprising 23 co-expression modules was successfully constructed. Among them, the "MEblue" module which had a highly positive correlation with ICA (R = 0.92, P = 4e-04). KEGG analysis showed that related genes were significantly enriched in the "Plant hormone signal transduction (ko04075)" pathway. YUC (a flavin-containing monooxygenase), AUX1, AUX/IAA, and ARF were identified as hub genes, and gene expression analysis showed that the expression levels of these hub genes under blue light were higher than those under white and red light. In addition, we also identified 6 auxin transport-related genes, including PIN1, PIN3, PIN4, PILS5, PILS6, and PILS7. Except PILS5, all of these genes showed the highest expression level under blue light. In conclusion, this study elucidated the molecular mechanism of light quality regulating adventitious root formation of tea short cutting through WGCNA analysis, which provided an innovation for "rapid seedling" of tea plants.

Identification of Hub Genes Associated With Non-alcoholic Steatohepatitis Using Integrated Bioinformatics Analysis

Background and aims: As a major cause of liver disease worldwide, non-alcoholic fatty liver disease (NAFLD) comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to understand its mechanisms. However, the etiology and mechanisms remain largely unknown. In addition, the gold standard for the diagnosis of NASH is liver biopsy, which is an invasive procedure. Therefore, there is a pressing need to develop noninvasive tests for NASH diagnosis. The goal of the study is to discover key genes involved in NASH development and investigate their value as noninvasive biomarkers.

Methods: The Gene Expression Omnibus (GEO) database was used to obtain two datasets encompassing NASH patients and healthy controls. We used weighted gene co-expression network analysis (WGCNA) and differential expression analysis in order to investigate the association between gene sets and clinical features, as well as to discover co-expression modules. A protein-protein interaction (PPI) network was created to extract hub genes. The results were validated using another publicly available dataset and mice treated with a high-fat diet (HFD) and carbon tetrachloride (CCl4).

Results: A total of 24 differentially co-expressed genes were selected by WGCNA and differential expression analysis. KEGG analysis indicated most of them were enriched in the focal adhesion pathway. GO analysis showed these genes were mainly enriched in circadian rhythm, aging, angiogenesis and response to drug (biological process), endoplasmic reticulum lumen (cellular component), and protein binding (molecular function). As a result, eight genes (JUN, SERPINE1, GINS2, TYMS, HMMR, IGFBP2, BIRC3, TNFRSF12A) were identified as hub genes. Finally, three genes were found significantly changed in both the validation dataset and the mouse model.

Conclusion: Our research discovered genes that have the potential to mediate the process of NASH and might be useful diagnostic biomarkers for the disorder.

E2F1-mediated GINS2 transcriptional activation promotes tumor progression through PI3K/AKT/mTOR pathway in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has high morbidity and mortality rates. It is therefore imperative to study the underlying mechanism of HCC to identify potential prognostic biomarkers and therapeutic targets. Recently, GINS2 has been identified to be a cancer-promoting gene in different cancer types. Nevertheless, the exact mechanism of GINS2 in HCC remains to be elucidated. To systematically explore the significance of GINS2, we first assessed the relative expression of GINS2 in pan-cancers based on data obtained from the HCCDB, TIMER, and TCGA databases. Then, we explored the clinical significance of GINS2 in HCC through Kaplan-Meier method as well as univariate and multivariate cox regression analysis. Additionally, functional enrichment analysis of GINS2 was done through GO, KEGG, PPI network, and immune cell infiltration analyses. Functional experiments were also conducted to investigate the biological significance of GINS2 in HCC cell lines. Our research revealed that GINS2 is involved in HCC progression and highlighted its potential value as a crucial diagnostic and therapeutic target for HCC.

Bioinformatical analysis identifies PDLIM3 as a potential biomarker associated with immune infiltration in patients with endometriosis

Background

Endometriosis is a chronic systemic disease, whose classic symptoms are pelvic pain and infertility. This disease seriously reduces the life quality of patients. The pathogenesis, recognition and treatment of endometriosis is still unclear, and cannot be over emphasized. The aim of our study was to investigate the potential biomarker of endometriosis for the mechanism and treatment.

Methods

Using GSE11691, GSE23339 and GSE5108 datasets, differentially expressed genes (DEGs) were identified between endometriosis and normal samples. The functions of DEGs were reflected by the analysis of gene ontology (GO), pathway enrichment and gene set enrichment analysis (GSEA). The LASSO regression model was performed to identify candidate biomarkers. The receiver operating characteristic curve (ROC) was used to evaluate discriminatory ability of candidate biomarkers. The predictive value of the markers in endometriosis were further validated in the GSE120103 dataset. Then, the expression level of biomarkers was detected by qRT-PCR and Western blot. Finally, the relationship between candidate biomarker expression and immune infiltration was estimated using CIBERSORT.

Results

A total of 42 genes were identified, which were mainly involved in cytokine-cytokine receptor interaction, systemic lupus erythematosus and chemokine signaling pathway. We confirmed PDLIM3 was a specific biomarker in endometriosis (AUC = 0.955) and validated in the GSE120103 dataset (AUC = 0.836). The mRNA and protein expression level of PDLIM3 in endometriosis tissue was significantly higher than normal. Immune cell infiltration analysis revealed that PDLIM3 was correlated with M2 macrophages, neutrophils, CD4+ memory resting T cells, gamma delta T cells, M1 Macrophages, resting mast cells, follicular helper T cells, activated NK cells, CD8+ T cells, regulatory T cells (Tregs), naive B cells, plasma cells and resting NK cells.