scholarly journals Identification of a Cancer Stemness Driven Prognostic Model and Therapeutic Drug Targets by Machine Learning in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Mingchun Lai ◽  
Bin Xi ◽  
Shenyu Wei ◽  
Wenjin Zhang ◽  
Shusen Zheng
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Differential Expression Analysis ◽  
Genomic Variation ◽  
Gene Set Enrichment Analysis ◽  
Therapeutic Drug ◽  
Response Analysis ◽  
Therapeutic Effects ◽  
Biological Processes ◽  
Cancer Stemness

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies. Cancer stem cells (CSCs), characterized by self-renewal and drug-resistance, play an important role in the development and progression of diverse cancers, but the underlying association of HCC and CSCs is not fully researched.Methods: Transcriptome and clinical data of 903 patients in four independent HCC cohorts were obtained from TCGA, ICGC, and GEO databases. We evaluated the stemlike index for each patient to reflect the cancer stemness by using one-class logistic regression (OCLR) algorithm. GISTIC 2.0, Maftools and GSVA were used to reveal the association between the stemness index and genomic variation and biological processes in HCC. The differential expression analysis, univariate Cox analysis and LASSO analysis were used to identify the prognostic stemness signatures. The HCC stemness-related risk score (HCSRS) was constructed to quantify stemness levels of individual tumors. Based on HCSRS, the nomogram was established for HCC prognosis in a quantitative approach. Additionally, single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to evaluate the immune infiltration levels in HCC, and drug response analysis was adopted to identify potential agents with drug sensitivity in high-HCSRS score patients.Results: The stemness index in HCC tissues was significantly higher than that in normal tissues, and there was a significant positive correlation with pathological grade. Patients with high stemness index showed higher somatic mutation frequency, tumor mutation load, and copy number variation frequency, and were significantly enriched in tumor-related signaling pathways. Meanwhile, the 7-gene based HCSRS model that was trained and validated in 4 independent cohorts exhibited high predictive significance for overall survival (OS). Further analysis revealed that patients with high HCSRS possessed higher immunosuppression status, characterized by significantly decreased infiltration of anti-tumor immune cells (CD8 T cells, cytotoxic T cells, DC cells, NK cells, etc.) and exhausted CYT responses. At last, a total of twelve agents were identified to have potential therapeutic effects in high-HCSRS patients.Conclusion: In current study, we systematically analyzed the potential relationship of HCC stemness with genomic variation, tumor microenvironment and biological processes, provided a theoretical basis for individualized treatment of HCC patients.

scholarly journals Bispecific c-Met/PD-L1 CAR-T Cells Have Enhanced Therapeutic Effects on Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Jiang ◽  
Tao Li ◽  
Jiaojiao Guo ◽  
Jingjing Wang ◽  
Lizhou Jia ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Carcinoma Cells ◽  
Therapeutic Effects ◽  
Single Chain ◽  
Antitumor Activities ◽  
Hepatocellular Carcinoma Cells ◽  
Car T Cells ◽  
Car T

T cells expressing chimeric antigen receptors, especially CD19 CAR-T cells have exhibited effective antitumor activities in B cell malignancies, but due to several factors such as antigen escape effects and tumor microenvironment, their curative potential in hepatocellular carcinoma has not been encouraging. To reduce the antigen escape risk of hepatocellular carcinoma, this study was to design and construct a bispecific CAR targeting c-Met and PD-L1. c-Met/PD-L1 CAR-T cells were obtained by lentiviral transfection, and the transfection efficiency was monitored by flow cytometry analysis. LDH release assays were used to elucidate the efficacy of c-Met/PD-L1 CAR-T cells on hepatocellular carcinoma cells. In addition, xenograft models bearing human hepatocellular carcinoma were constructed to detect the antitumor effect of c-Met/PD-L1 CAR-T cells in vivo. The results shown that this bispecific CAR was manufactured successfully, T cells modified with this bispecific CAR demonstrated improved antitumor activities against c-Met and PD-L1 positive hepatocellular carcinoma cells when compared with those of monovalent c-Met CAR-T cells or PD-L1 CAR-T cells but shown no distinct cytotoxicity on hepatocytes in vitro. In vivo experiments shown that c-Met/PD-L1 CAR-T cells significantly inhibited tumor growth and improve survival persistence compared with other groups. These results suggested that the design of single-chain, bi-specific c-Met/PD-L1 CAR-T is more effective than that of monovalent c-Met CAR-T for the treatment of hepatocellular carcinoma., and this bi-specific c-Met/PD-L1 CAR is rational and implementable with current T-cell engineering technology.

scholarly journals A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Dengliang Lei ◽  
Yue Chen ◽  
Yang Zhou ◽  
Gangli Hu ◽  
Fang Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

scholarly journals MAPK-RAP1A Signaling Enriched in Hepatocellular Carcinoma Is Associated With Favorable Tumor-Infiltrating Immune Cells and Clinical Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hailin Li ◽  
Guangyu Han ◽  
Xing Li ◽  
Bowen Li ◽  
Bo Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
T Cell ◽  
Immune Cells ◽  
Clinical Characteristics ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Gamma Delta ◽  
Gene Sets ◽  
Prognostic Prediction

BackgroundMAPK-RAP1A signaling, which is involved in cancer progression, remains to be defined. Upregulation of MAPK-RAP1A signaling accounts for most cancers that harbor high incident rate, such as non-small cell lung cancer (NSCLC) and pancreatic cancer, especially in hepatocellular carcinoma (HCC). MAPK-RAP1A signaling plays an important function as clinical diagnosis and prognostic value in cancers, and the role of MAPK-RAP1A signaling related with immune infiltration for HCC should be elucidated.MethodsMicroarray data and patient cohort information from The Cancer Genome Atlas (TCGA; n = 425) and International Cancer Genome Consortium (ICGC; n = 405) were selected for validation. The Cox regression and least absolute shrinkage and selection operator (LASSO) were used to construct a clinical prognostic model in this analysis and validation study. We also tested the area under the curve (AUC) of the risk signature that could reflect the status of predictive power by determining model. MAPK-RAP1A signaling is also associated with tumor-infiltrating immune cells (TICs) as well as clinical parameters in HCC. The GSEA and CIBERSORT were used to calculate the proportion of TICs, which should be beneficial for the clinical characteristics (clinical stage, distant metastasis) and positively correlated with the survival of HCC patients.ResultsHCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), and STMN1, RAP1A, FLT3, HSPA8, ANGPT2, and PGF were used as candidate biomarkers for risk scores of HCC. To determine the molecular mechanism of this signature gene association, Gene Set Enrichment Analysis (GSEA) was proposed. Cytokine–cytokine receptor interaction, TGF-β signaling pathway, and Intestinal immune network for IgA production gene sets were closely related in MAPK-RAP1A gene sets. Thus, we established a novel prognostic prediction of HCC to deepen learning of MAPK-RAP1A signaling pathways.ConclusionOur findings demonstrated that HCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), which may be a novel prognostic prediction of HCC.

scholarly journals SOCS2 serves as a prognostic indicator and is regulated by miR-7-5p in hepatocellular carcinoma

2020 ◽  
Author(s):  
Lu Liu ◽  
Kangkang Yu ◽  
Yahui Zheng ◽  
Meisi Huo ◽  
Hao Zhao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Survival Analysis ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Prognostic Indicator ◽  
Cytokine Signaling ◽  
Student’S T ◽  
Student’S T Test

Abstract Background Suppressor of cytokine signaling (SOCS) family members are essential components of negative regulation of cytokine signaling known to be involved in occurrence and progression of hepatocellular carcinoma (HCC), while a comprehensive analysis of the correlation between SOCS family members and HCC has not yet been elucidated. Methods Differential expression analysis of SOCS genes was performed on TIMER, which was further validated by GSE94660 dataset from Gene Expression Omnibus (GEO). Prognostic values of SOCS genes were analyzed by TIMER and GEPIA. TISIDB was used to assess association between SOCS2 expression, clinical stages and pathological grades of HCC, as well as SOCS2 expression across immune subtypes and iClusters. Differential expression of genes (DEGs) identification was tested by two-tail student’s t test using The Cancer Genome Atlas (TCGA) RNA-seq of HCC. And functional annotation of the DEGs was performed by Metascape. Fraction of Immune cells was estimated by CIBERSORT, and infiltration difference were compared by two-tail student’s t test. Genetic alteration identification and promoter methylation evaluation were analyzed by cBioPortal and DNMIVD, respectively. Starbase was used to predict potential miRNAs that target SOCS2. Differential expressions of candidate miRNAs were analyzed by dbMEMC, which was further validated by GSE22058 from GEO. Survival analysis of miRNA was performed with KM Plotter. Results Differential expression analysis showed SOCS2 and SOCS3 were significantly downregulated, while SOCS5 and SOCS7 were upregulated in HCC. Survival analysis revealed only SOCS2 mRNA had significant prognostic value in terms of overall survival and disease-free survival in HCC. Specifically, higher SOCS2 predicted improved outcome. Significant correlations were found between SOCS2 and pathological stage, grade, molecular subtypes and immune subtypes. When comparing SOCS2high versus SOCS2low patients, the DEGs were functionally enriched in metabolism of RNA, organic cyclic compound catabolic process, and rRNA processing in the nucleus and cytosol. Immune cell infiltration analysis showed resting memory CD4 T cells, γδ T cells, follicular helper T cells, regulatory T cells and M0 macrophages were associated with SOCS2 expression. Mechanistically, miR-7-5p was the potential contributor responsible for downregulation of SOCS2. Conclusions SOCS2 could be a promising prognostic indicator and a potential therapeutic target for HCC.

scholarly journals Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9201
Author(s):  
Zhipeng Zhu ◽  
Lulu Li ◽  
Jiuhua Xu ◽  
Weipeng Ye ◽  
Borong Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Biological Processes ◽  
Cox Regression Analysis ◽  
Diagnostic Capability

Background Due to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). Methods Differentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in four types of testing cohorts. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance. Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group. Results Ten prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis. Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide new insights into the development of HCC. Conclusion The study have identified ten metabolic prognostic genes and developed a prognostic gene signature to provide more powerful prognostic information and improve the survival prediction for HCC.

scholarly journals In vivo therapeutic effects of affinity-improved-TCR engineered T-cells on HBV-related hepatocellular carcinoma

2020 ◽  
Vol 8 (2) ◽  
pp. e001748
Author(s):  
Qi Liu ◽  
Ye Tian ◽  
Yanyan Li ◽  
Wei Zhang ◽  
Wenxuan Cai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Mouse Models ◽  
In Vivo Studies ◽  
Therapeutic Effects ◽  
Wild Type ◽  
Antigen Specificity ◽  
Wide Range ◽  
Engineered T Cells

BackgroundIn patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), virus-specific cytotoxic T lymphocytes (CTLs) fail to eliminate HCC cells expressing HBV antigens. As the expression of viral antigen in HBV-associated HCC may decrease to allow tumor to escape immune attacks, we hypothesized that an HBV surface antigen (HBsAg)-specific affinity-improved-T-cell receptor (TCR) will enable T cells to target HCC more effectively than corresponding wild-type-TCR. We also postulated that TCR promiscuity can be exploited to efficiently capture HBV variants that can hinder CTL-based therapeutics.MethodsWe applied flexi-panning to isolate affinity-improved TCRs binding to a variant antigen, the human leukocyte antigen (HLA)-A*02:01-restricted nonapeptide HBs371-379-ILSPFLPLL, from libraries constructed with a TCR cloned using the decapeptide HBs370-379-SIVSPFIPLL. The potency and safety of the affinity-improved-TCR engineered T-cells (Ai-TCR-T) were verified with potentially cross-reactive human and HBV-variant peptides, tumor and normal cells, and xenograft mouse models.ResultsAi-TCR-T cells retained cognate HBV antigen specificity and recognized a wide range of HBV genotypic variants with improved sensitivity and cytotoxicity. Cell infusions produced complete elimination of HCC without recurrence in the xenograft mouse models. Elevated accumulation of CD8+ Ai-TCR-T cells in tumors correlated with tumor shrinkage.ConclusionThe in vitro and in vivo studies demonstrated that HBsAg-specific Ai-TCR-T cells had safety profiles similar to those of their wild-type counterparts and significantly enhanced potency. This study presents an approach to develop new therapeutic strategies for HBV-related HCC.

scholarly journals Construction of a Ferroptosis-Related Nine-lncRNA Signature for Predicting Prognosis and Immune Response in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhijie Xu ◽  
Bi Peng ◽  
Qiuju Liang ◽  
Xi Chen ◽  
Yuan Cai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
High Risk ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature

Ferroptosis is an iron-dependent cell death process that plays important regulatory roles in the occurrence and development of cancers, including hepatocellular carcinoma (HCC). Moreover, the molecular events surrounding aberrantly expressed long non-coding RNAs (lncRNAs) that drive HCC initiation and progression have attracted increasing attention. However, research on ferroptosis-related lncRNA prognostic signature in patients with HCC is still lacking. In this study, the association between differentially expressed lncRNAs and ferroptosis-related genes, in 374 HCC and 50 normal hepatic samples obtained from The Cancer Genome Atlas (TCGA), was evaluated using Pearson’s test, thereby identifying 24 ferroptosis-related differentially expressed lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model were used to construct and validate a prognostic risk score model from both TCGA training dataset and GEO testing dataset (GSE40144). A nine-lncRNA-based signature (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) was identified as the ferroptosis-related prognostic model for HCC, independent of multiple clinicopathological parameters. In addition, the HCC patients were divided into high-risk and low-risk groups according to the nine-lncRNA prognostic signature. The gene set enrichment analysis enrichment analysis revealed that the lncRNA-based signature might regulate the HCC immune microenvironment by interfering with tumor necrosis factor α/nuclear factor kappa-B, interleukin 2/signal transducers and activators of transcription 5, and cytokine/cytokine receptor signaling pathways. The infiltrating immune cell subtypes, such as resting memory CD4(+) T cells, follicular helper T cells, regulatory T cells, and M0 macrophages, were all significantly different between the high-risk group and the low-risk group as indicated in Spearman’s correlation analysis. Moreover, a substantial increase in the expression of B7H3 immune checkpoint molecule was found in the high-risk group. Our findings provided a promising insight into ferroptosis-related lncRNAs in HCC and a personalized prediction tool for prognosis and immune responses in patients.

scholarly journals Contributions and Prognostic Values of N6-Methyladenosine RNA Methylation Regulators in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Li-Wen Qi ◽  
Jian-Hui Jia ◽  
Chen-Hao Jiang ◽  
Jian-Ming Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Rna Binding ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Biological Processes ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Free Interval ◽  
Cancer Types

IntroductionThe methylation at position N6 of adenine is called N6-methyladenosine (m6A). This transcriptional RNA modification exerts a very active and important role in RNA metabolism and in other biological processes. However, the activities of m6A associated with malignant liver hepatocellular carcinoma (LIHC) are unknown and are worthy of study.Materials and MethodsUsing the data of University of California, Santa Cruz (UCSC), the expression of M6A methylation regulators in pan-cancer was evaluated as a screening approach to identify the association of M6A gene expression and 18 cancer types, with a specific focus on LIHC. LIHC datasets of The Cancer Genome Atlas (TCGA) were used to explore the expression of M6A methylation regulators and their clinical significance. Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the underlying mechanism based on the evaluation of aberrant expression of m6A methylation regulators.ResultsThe expression alterations of m6A-related genes varied across cancer types. In LIHC, we found that in univariate Cox regression analysis, up-regulated m6A modification regulators were associated with worse prognosis, except for ZC3H13. Kaplan–Meier survival curve analysis indicated that higher expression of methyltransferase-like protein 3 (METTL3) and YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) genes related to the worse survival rate defined by disease-related survival (DSS), overall survival (OS), progression-free interval (PFI), and disease-free interval (DFI). Up-regulated m6A methylation regulator group (cluster2) obtained by consensus clustering was associated with poor prognosis. A six-gene prognostic signature established using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm performed better in the early (I + II; T1 + T2) stages than in the late (III + IV; T3 + T4) stages of LIHC. Using the gene signature, we constructed a risk score and found that it was an independent predictive factor for prognosis. Using GSEA, we identified processes involved in DNA damage repair and several biological processes associated with malignant tumors that were closely related to the high-risk group.ConclusionIn summary, our study identified several genes associated with m6A in LIHC, especially METTL3 and YTHDF1, and confirmed that a risk signature comprised of m6A-related genes was able to forecast prognosis.

scholarly journals Identification of Hub Biomarkers and Immune-Related Pathways Participating in the Progression of Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis

2022 ◽  
Vol 12 ◽  
Author(s):  
Meng-Di Xia ◽  
Rui-Ran Yu ◽  
Dong-Ming Chen
Keyword(s):  
T Cells ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Antineutrophil Cytoplasmic Antibody ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Signal Pathways ◽  
Hub Genes ◽  
Gene Markers ◽  
Differential Genes

BackgroundAntineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that generally induces the progression of rapidly progressive glomerulonephritis (GN). The purpose of this study was to identify key biomarkers and immune-related pathways involved in the progression of ANCA-associated GN (ANCA-GN) and their relationship with immune cell infiltration.MethodsGene microarray data were downloaded from the Gene Expression Omnibus (GEO). Hub markers for ANCA-GN were mined based on differential expression analysis, weighted gene co-expression network analysis (WGCNA) and lasso regression, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) of the differential genes. The infiltration levels of 28 immune cells in the expression profile and their relationship to hub gene markers were analysed using single-sample GSEA (ssGSEA). In addition, the accuracy of the hub markers in diagnosing ANCA-GN was subsequently evaluated using the receiver operating characteristic curve (ROC).ResultsA total of 651 differential genes were screened. Twelve co-expression modules were obtained via WGCNA; of which, one hub module (black module) had the highest correlation with ANCA-GN. A total of 66 intersecting genes were acquired by combining differential genes. Five hub genes were subsequently obtained by lasso analysis as potential biomarkers for ANCA-GN. The immune infiltration results revealed the most significant relationship among monocytes, CD4+ T cells and CD8+ T cells. ROC curve analysis demonstrated a prime diagnostic value of the five hub genes. According to the functional enrichment analysis of the differential genes, hub genes were mainly enhanced in immune- and inflammation-related pathways.ConclusionB cells and monocytes were closely associated with the pathogenesis of ANCA-GN. Hub genes (CYP3A5, SLC12A3, BGN, TAPBP and TMEM184B) may be involved in the progression of ANCA-GN through immune-related signal pathways.

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