Molecular Characteristics of Choledochal Cysts in Children: Transcriptome Sequencing

A choledochal cyst (CC) is a common congenital biliary disease in children, yet the underlying molecular bases for the cystic and fusiform clinical subtypes are unknown. RNA sequencing (RNA-seq) has been performed on 22 high-quality CC samples, including 12 cystic CC and 10 fusiform CC samples, to search for molecular features. Weighted gene co-expression network analysis (WGCNA) was performed to identify key modules associated with clinical subtypes. Bioinformatic analyses were conducted to elucidate potential mechanisms. Then, we constructed protein–protein interaction (PPI) networks to identify candidate hub genes related to CC. Finally, we used the support vector machine (SVM) to eliminate redundant features and screen out the hub genes. The selected gene expression was determined in CC patients through quantitative real-time polymerase chain reaction (PCR). A total of 6,463 genes were found to be aberrantly expressed between cystic CC and fusiform CC. Twelve co-expression modules that correlated with clinical subtypes of CC were identified and assigned representative colors. Among the 12 modules, the blue module was considered the key module. Two functionally distinct sets of dysregulated genes have been identified in two major subtypes, metabolism-related genes in cystic CC and immune-related genes in fusiform CC. A total of 20 candidate hub genes that were correlated with clinical subtypes were found in the blue module. In addition, we found ERBB2 and WNT11 that have not been studied in CC and verified their differential expression in CC through quantitative real-time PCR experiments. For the first time, we have described the transcriptome characteristics of CC. These results suggest that cystic CC and fusiform CC have different molecular mechanisms. The bi-omics-identified novel candidate genes and pathways might be helpful for personalized treatment and are of great clinical significance for CC.

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Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

10.21203/rs.3.rs-512609/v1 ◽

2021 ◽

Author(s):

Jian Lin ◽

Yuanhua Lu ◽

Bizhou Wang ◽

Ping Jiao ◽

Jie Ma

Keyword(s):

Gene Expression ◽

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Type 1 Diabetes Mellitus ◽

Nk Cells ◽

Immune Cells ◽

Support Vector ◽

Hub Genes ◽

Immune Related Genes

Abstract Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO) - support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

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A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

10.21203/rs.3.rs-19035/v1 ◽

2020 ◽

Author(s):

Yanjie Han ◽

Xinxin Li ◽

Jiliang Yan ◽

Chunyan Ma ◽

Xin Wang ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Growth Factor Receptor ◽

Principal Component ◽

Distant Metastases ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Hub Genes ◽

Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

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Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽

10.1371/journal.pone.0257343 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257343

Author(s):

Shaoshuo Li ◽

Baixing Chen ◽

Hao Chen ◽

Zhen Hua ◽

Yang Shao ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Network Analysis ◽

Postmenopausal Osteoporosis ◽

Molecular Mechanism ◽

Molecular Mechanisms ◽

Functional Enrichment ◽

Support Vector ◽

Hub Genes ◽

Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

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Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

10.21203/rs.3.rs-54056/v2 ◽

2021 ◽

Author(s):

Fu Jun Liao ◽

Peng-Fei Zheng ◽

Yao-Zong Guan ◽

Hong Wei Pan ◽

Wei Li

Keyword(s):

Molecular Mechanisms ◽

Acid Metabolism ◽

Unsaturated Fatty Acids ◽

Microarray Dataset ◽

Hub Genes ◽

Online Tool ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Qpcr Analysis ◽

Blue Module

Abstract Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.Methods: The microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.Results: The significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.Conclusions: SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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A Comprehensive Network Pharmacology-Based Strategy to Investigate Multiple Mechanisms of HeChan Tablet on Lung Cancer

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/7658342 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Zhenjie Zhuang ◽

Qianying Chen ◽

Cihui Huang ◽

Junmao Wen ◽

Haifu Huang ◽

...

Keyword(s):

Lung Cancer ◽

Therapeutic Target ◽

Molecular Mechanisms ◽

Target Genes ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Network Pharmacology ◽

Hub Genes ◽

Lung Cancer Patients ◽

Ppi Networks

Background. HeChan tablet (HCT) is a traditional Chinese medicine preparation extensively prescribed to treat lung cancer in China. However, the pharmacological mechanisms of HCT on lung cancer remain to be elucidated. Methods. A comprehensive network pharmacology-based strategy was conducted to explore underlying mechanisms of HCT on lung cancer. Putative targets and compounds of HCT were retrieved from TCMSP and BATMAN-TCM databases; related genes of lung cancer were retrieved from OMIM and DisGeNET databases; known therapeutic target genes of lung cancer were retrieved from TTD and DrugBank databases; PPI networks among target genes were constructed to filter hub genes by STRING. Furthermore, the pathway and GO enrichment analysis of hub genes was performed by clusterProfiler, and the clinical significance of hub genes was identified by The Cancer Genome Atlas. Result. A total of 206 compounds and 2,433 target genes of HCT were obtained. 5,317 related genes of lung cancer and 77 known therapeutic target genes of lung cancer were identified. 507 unique target genes were identified among HCT-related genes of lung cancer and 34 unique target genes were identified among HCT-known therapeutic target genes of lung cancer. By PPI networks, 11 target genes AKT1, TP53, MAPK8, JUN, EGFR, TNF, INS, IL-6, MYC, VEGFA, and MAPK1 were identified as major hub genes. IL-6, JUN, EGFR, and MYC were shown to associate with the survival of lung cancer patients. Five compounds of HCT， quercetin, luteolin, kaempferol, beta-sitosterol, and baicalein were recognized as key compounds of HCT on lung cancer. The gene enrichment analysis implied that HCT probably benefitted patients with lung cancer by modulating the MAPK and PI3K-Akt pathways. Conclusion. This study predicted pharmacological and molecular mechanisms of HCT against lung cancer and could pave the way for further experimental research and clinical application of HCT.

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Integration of Gene Expression Profile Data of Human Epicardial Adipose Tissue from Coronary Artery Disease to Verification of Hub Genes and Pathways

BioMed Research International ◽

10.1155/2019/8567306 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Weitie Wang ◽

Qing Liu ◽

Yong Wang ◽

Hulin Piao ◽

Bo Li ◽

...

Keyword(s):

Coronary Artery Disease ◽

Adipose Tissue ◽

Coronary Artery ◽

Focal Adhesion ◽

Molecular Mechanisms ◽

Epicardial Adipose Tissue ◽

Hub Genes ◽

Qrt Pcr ◽

Ppi Networks ◽

Artery Disease

Background. This study aim to identify the core pathogenic genes and explore the potential molecular mechanisms of human coronary artery disease (CAD). Methodology. Two gene profiles of epicardial adipose tissue from CAD patients including GSE 18612 and GSE 64554 were downloaded and integrated by R software packages. All the coexpression of deferentially expressed genes (DEGs) were picked out and analyzed by DAVID online bioinformatic tools. In addition, the DEGs were totally typed into protein-protein interaction (PPI) networks to get the interaction data among all coexpression genes. Pictures were drawn by cytoscape software with the PPI networks data. CytoHubba were used to predict the hub genes by degree analysis. Finally all the top 10 hub genes and prediction genes in Molecular complex detection were analyzed by Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis. qRT-PCR were used to identified all the 10 hub genes. Results. The top 10 hub genes calculated by the degree method were AKT1, MYC, EGFR, ACTB, CDC42, IGF1, FGF2, CXCR4, MMP2 and LYN, which relevant with the focal adhesion pathway. Module analysis revealed that the focal adhesion was also acted an important role in CAD, which was consistence with cytoHubba. All the top 10 hub genes were verified by qRT-PCR which presented that AKT1, EGFR, CDC42, FGF2, and MMP2 were significantly decreased in epicardial adipose tissue of CAD samples (p<0.05) and MYC, ACTB, IGF1, CXCR4, and LYN were significantly increased (p<0.05). Conclusions. These candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of CAD.

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Identification of Hub Genes related to the Progression of Bladder Cancer by an Integrated Bioinformatics Analysis

10.21203/rs.3.rs-819606/v1 ◽

2021 ◽

Author(s):

Han Wang ◽

Jieqing Chen ◽

Xinhui Liao ◽

Yang Liu ◽

Aifa Tang ◽

...

Keyword(s):

Bladder Cancer ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Signalling Pathway ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Hub Genes ◽

Immune Infiltration ◽

Ppi Networks ◽

Normal Bladder

Abstract BACKGROUND and OBJECTIVE: A better understanding of the molecular mechanisms underlying bladder cancer is necessary to identify candidate therapeutic targets. METHODS: We screened for genes associated with bladder cancer progression and prognosis. Publicly available expression data were obtained from TCGA and GEO to identify differentially expressed genes (DEGs) between bladder cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations between hub genes and immune infiltration and immune therapy were evaluated. RESULTS: 3461 DEGs in TCGA-BC and 1069 DEGs in the GSE dataset were identified, with 87 overlapping differentially expressed genes between the bladder cancer and normal bladder groups. Hub genes in the tumour group were mainly enriched for cell proliferation-related GO terms and KEGG pathways, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. PPI networks for the genes identified in the normal and tumour groups were constructed. Based on a survival analysis, CDH19, RELN, PLP1, and TRIB3 were significantly associated with prognosis (P < 0.05). Four hub genes were significantly enriched in the MAPK signalling pathway, VEGF signalling pathway, WNT signalling pathway, cell cycle, and P53 signalling pathway based on a gene set enrichment analysis; these genes were associated with immune infiltration levels in bladder cancer. CONCLUSIONS: CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of bladder cancer and are potential therapeutic and prognostic targets.

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In silico Identification of 10 Hub Genes and an miRNA–mRNA Regulatory Network in Acute Kawasaki Disease

Frontiers in Genetics ◽

10.3389/fgene.2021.585058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jin Ma ◽

Huan Gui ◽

Yunjia Tang ◽

Yueyue Ding ◽

Guanghui Qian ◽

...

Keyword(s):

Kawasaki Disease ◽

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Unknown Etiology ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Kawasaki disease (KD) causes acute systemic vasculitis and has unknown etiology. Since the acute stage of KD is the most relevant, the aim of the present study was to identify hub genes in acute KD by bioinformatics analysis. We also aimed at constructing microRNA (miRNA)–messenger RNA (mRNA) regulatory networks associated with acute KD based on previously identified differentially expressed miRNAs (DE-miRNAs). DE-mRNAs in acute KD patients were screened using the mRNA expression profile data of GSE18606 from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DE-mRNAs were performed with the DAVID database. Target genes of DE-miRNAs were predicted using the miRWalk database and their intersection with DE-mRNAs was obtained. From a protein–protein interaction (PPI) network established by the STRING database, Cytoscape software identified hub genes with the two topological analysis methods maximal clique centrality and Degree algorithm to construct a miRNA-hub gene network. A total of 1,063 DE-mRNAs were identified between acute KD and healthy individuals, 472 upregulated and 591 downregulated. The constructed PPI network with these DE-mRNAs identified 38 hub genes mostly enriched in pathways related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, osteoclast differentiation, adipocytokine signaling pathway and tumor necrosis factor signaling pathway. Target genes were predicted for the up-regulated and down-regulated DE-miRNAs, 10,203, and 5,310, respectively. Subsequently, 355, and 130 overlapping target DE-mRNAs were obtained for upregulated and downregulated DE-miRNAs, respectively. PPI networks with these target DE-mRNAs produced 15 hub genes, six down-regulated and nine upregulated hub genes. Among these, ten genes (ATM, MDC1, CD59, CD177, TRPM2, FCAR, TSPAN14, LILRB2, SIRPA, and STAT3) were identified as hub genes in the PPI network of DE-mRNAs. Finally, we constructed the regulatory network of DE-miRNAs and hub genes, which suggested potential modulation of most hub genes by hsa-miR-4443 and hsa-miR-6510-5p. SP1 was predicted to potentially regulate most of DE-miRNAs. In conclusion, several hub genes are associated with acute KD. An miRNA–mRNA regulatory network potentially relevant for acute KD pathogenesis provides new insights into the underlying molecular mechanisms of acute KD. The latter may contribute to the diagnosis and treatment of acute KD.

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Identification of the Core MicroRNAs and Potential Molecular Mechanismsin Sarcoidosis Using Bioinformatics Analysis

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.644232 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yuan Cao ◽

Hua Zhang ◽

Lulu Zheng ◽

Qiao Li

Keyword(s):

Regulatory Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Hub Genes ◽

The Core ◽

Ppi Networks ◽

Go Enrichment ◽

Mirnas Expression ◽

Go Enrichment Analysis ◽

Insight Into

Sarcoidosis is a systemic heterogeneous inflammatory disease; however, the etiology and pathogenesis of sarcoidosis are still unknown. Herein, we investigated the core microRNAs and potential molecular mechanisms in sarcoidosis. The DE-miRNAs were diagnosed using the LIMMA software package. DIANA-mirPath was employed to perform pathway and GO enrichment analysis of the DE-miRNAs. PPI networks and miRNA-target gene regulatory networks were used to obtain insight into the actions of DE-miRNAs. Expression of the hub genes along with miRNAs was validated in clinical specimens. Overall, 266 DE-miRNAs were screened. Among these DE-miRNAs, hsa-miR-144, hsa-miR-126, as well as hsa-miR-106a were the upmost upregulated miRNAs; hsa-miR-151-3p, hsa-miR-320d, and hsa-miR-324-3p were the top downregulated miRNAs. NR3C1, ZBTB7A, NUFIP2, BZW1, ERGIC2, and VEGFA were mapped as the most targeted hub genes in the upregulation of miRNAs, and MCL1 and SAE1 were the most targeted hub genes in the downregulation of miRNA. VEGFA and NR3C1 were selected and potentially modulated by hsa-miR-20b, hsa-miR-126, and hsa-miR-106a. In sarcoidosis pathological tissue, hsa-miR-126 was highly expressed, and VEGFA and NR3C1 were overexpressed. In conclusion, our results revealed the dysregulation of hsa-miR-126 and a potential regulatory mechanism for pathogenesis in sarcoidosis.

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Progress in molecular feature of smoldering mantle cell lymphoma

Experimental Hematology and Oncology ◽

10.1186/s40164-021-00232-3 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Panruo Jiang ◽

Aakash Desai ◽

Haige Ye

Keyword(s):

Mantle Cell Lymphoma ◽

Molecular Mechanisms ◽

Cell Lymphoma ◽

Low Frequency ◽

Molecular Characteristics ◽

Ki 67 ◽

Molecular Feature ◽

Molecular Features ◽

Mantle Cell ◽

Low Expression

AbstractMantle cell lymphoma (MCL) is considered one of the most aggressive lymphoid tumors. However, it sometimes displays indolent behavior in patients and might not necessitate treatment at diagnosis; this has been described as “smoldering MCL” (SMCL). There are significant differences in the diagnosis, prognosis, molecular mechanisms and treatments of indolent MCL and classical MCL. In this review, we discuss the progress in understanding the molecular mechanism of indolent MCL to provide insights into the genomic nature of this entity. Reported findings of molecular features of indolent MCL include a low Ki-67 index, CD200 positivity, a low frequency of mutations in TP53, a lack of SOX11, normal arrangement and expression of MYC, IGHV mutations, differences from classical MCL by L-MCL16 assays and MCL35 assays, an unmutated P16 status, few defects in ATM, no NOTCH1/2 mutation, Amp 11q gene mutation, no chr9 deletion, microRNA upregulation/downregulation, and low expression of several genes that have been valued in recent years (SPEN, SMARCA4, RANBP2, KMT2C, NSD2, CARD11, FBXW7, BIRC3, KMT2D, CELSR3, TRAF2, MAP3K14, HNRNPH1, Del 9p and/or Del 9q, SP140 and PCDH10). Based on the above molecular characteristics, we may distinguish indolent MCL from classical MCL. If so, indolent MCL will not be overtreated, whereas the treatment of classical MCL will not be delayed.

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