TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer

Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein-protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan-Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients.

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Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17041373 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1373 ◽

Cited By ~ 2

Author(s):

Md. Ali Hossain ◽

Tania Akter Asa ◽

Md. Mijanur Rahman ◽

Shahadat Uddin ◽

Ahmed A. Moustafa ◽

...

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Thyroid Adenoma ◽

Hub Genes ◽

Microarray Gene Expression ◽

Genetic Profiling ◽

Follicular Thyroid Adenoma ◽

Pathway Analyses

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

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Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

BioMed Research International ◽

10.1155/2020/8959210 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Jianxia Wei ◽

Yang Wang ◽

Kejian Shi ◽

Ying Wang

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Protein Interactions ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Protein Protein Interactions ◽

Normal Tissues ◽

Interactive Analysis ◽

Kaplan Meier ◽

Search Tool

Purposes. Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes. Results. The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues. Conclusion. We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Journal of Diabetes Research ◽

10.1155/2021/5546199 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Bojun Xu ◽

Lei Wang ◽

Huakui Zhan ◽

Liangbin Zhao ◽

Yuehan Wang ◽

...

Keyword(s):

Gene Expression ◽

Diabetic Nephropathy ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Topological Analysis ◽

Gene Expression Omnibus ◽

Complement Cascade ◽

Hub Genes ◽

Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

10.21203/rs.3.rs-595148/v1 ◽

2021 ◽

Author(s):

Hongpeng Fang ◽

Zhansen Huang ◽

Xianzi Zeng ◽

Jiaming Wan ◽

Jieying Wu ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Transcriptional Level ◽

Hub Genes ◽

Kaplan Meier ◽

High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

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OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma

Genes ◽

10.3390/genes11121523 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1523

Author(s):

Huimin Li ◽

Longxiang Xie ◽

Qiang Wang ◽

Yifang Dang ◽

Xiaoxiao Sun ◽

...

Keyword(s):

Gene Expression ◽

Poor Prognosis ◽

Analysis Data ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Rank Test ◽

Web Tool ◽

Kaplan Meier ◽

Cox Analysis

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

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Prospective pathway signaling and prognostic values of MicroRNA-9 in ovarian cancer based on gene expression omnibus (GEO): a bioinformatics analysis

Journal of Ovarian Research ◽

10.1186/s13048-021-00779-z ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Li Zuo ◽

Xiaoli Li ◽

Yue Tan ◽

Hailong Zhu ◽

Mi Xiao

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Literature Review ◽

Poor Prognosis ◽

Target Genes ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Low Expression

Abstract Objective MicroRNAs (miRNAs) play a vital role in the development of ovarian cancer (OC). The aim of this study to investigate the prognostic value and potential signaling pathways of hsa-miR-9-5p (miR-9) in OC through literature review and bioinformatics methods. Methods The expression of miR-9 in OC was assessed using the public datasets from the Gene Expression Omnibus (GEO) database. And a literature review was also performed to investigate the correlation between miR-9 expression and the OC prognosis. Two mRNA datasets (GSE18520 and GSE36668) of OC tissues and normal ovarian tissues (NOTs) were downloaded from GEO to identify the differentially expressed genes (DEGs). The target genes of hsa-miR-9-5p (TG-miR-9-5p) were predicted using miRWALK3.0 and TargetScan. Then the gene overlaps between DEGs in OC and the predicted TG-miR-9-5p were confirmed using a Venn diagram. After that, overlapping genes were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the impact of hub genes on OC prognosis was analyzed. Results It was found that OC patients with miR-9 low expression had poor prognosis. A total of 107 DEGs related to both OC and miR-9 were identified. Dozens of DEGs were enriched in developmental process, extracellular matrix structural constituent, cell junction, axon guidance. In the PPI network analysis, 5 of the top 10 hub genes was significantly associated with decreased overall survival of OC patients, namely FBN1 (HR = 1.64, P < 0.05), PRRX1 (HR = 1.76, P < 0.05), SMC2 (HR = 1.22, P < 0.05), SMC4 (HR = 1.31, P < 0.05), and VCAN (HR = 1.48, P < 0.05). Conclusion Low expression of miR-9 indicates poor prognosis of OC patients. MiR-9 plays a crucial role in the biological process of OC by binding to target genes, thus affecting the prognosis of patients.

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An independent poor-prognosis subtype of hepatocellular carcinoma based on the tumor microenvironment

Journal of International Medical Research ◽

10.1177/0300060520980646 ◽

2021 ◽

Vol 49 (2) ◽

pp. 030006052098064

Author(s):

Junfeng Wang ◽

Jianying Lou ◽

Lei Fu ◽

Qu Jin

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Tumor Microenvironment ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

Immune Cell ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Using Data

Background Hepatocellular carcinoma (HCC) is a highly malignant tumor with a particularly poor prognosis. The tumor microenvironment (TME) is closely associated with tumorigenesis, progression, and treatment. However, the relationship between TME genes and HCC patient prognosis is poorly understood. Methods In this study, we identified two prognostic subtypes based on the TME using data from The Cancer Genome Atlas and Gene Expression Omnibus. The Microenvironment Cell Populations-counter method was used to evaluate immune cell infiltration in HCC. Differentially expressed genes between molecular subtypes were calculated with the Limma package, and clusterProfiler was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses to identify genes related to the independent subtypes. We also integrated mRNA expression data into our bioinformatics analysis. Results We identified 4227 TME-associated genes and 640 genes related to the prognosis of HCC. We defined two major subtypes (Clusters 1 and 2) based on the analysis of TME-associated gene expression. Cluster 1 was characterized by increased expression of immune-associated genes and a worse prognosis than Cluster 2. Conclusions The identification of these HCC subtypes based on the TME provides further insight into the molecular mechanisms and prediction of HCC prognosis.

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Genes co-related with poor prognosis of patients with lung cancer via bioinformatical approaches

10.21203/rs.3.rs-17941/v1 ◽

2020 ◽

Author(s):

Dai Shi ◽

Yeming Han ◽

Xin Wang ◽

Guihua Hou

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Poor Prognosis ◽

Malignant Tumors ◽

Basic Research ◽

Underlying Mechanism ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Normal Lung ◽

Cancer Tissue

Abstract Lung cancer is one of the most common malignant tumors with high mortality worldwide. Recently, researchers reported that molecular markers on lung cancer could be used as diagnostic and prognostic targets. However, these molecules were not ideal in specificity and high selectivity. Therefore, exploring more reliable biomarkers to improve the prognosis and clarify the underlying mechanism is urgently needed both for clinic and basic research. This study aimed to identify significant genes with poor prediction for lung cancer and their underlying mechanisms. Firstly, we used gene expression datasets available from GEO (Gene Expression Omnibus) database. There were 109 lung cancer samples and 27 normal samples in the selected datasets. First, DEGs (Different Expressed Gene set) of lung cancer and normal lung samples were screen out with GEO2R tool, and we displayed them by Venn diagram software and Heatmap. Secondly, we used DAVID (Database for Annotation, Visualization and Integrated Discovery) to analyze KEGG (Kyoto Encyclopedia of Gene and Genome) pathway and GO (Gene Ontology). Third, PPI (Protein-Protein Interaction) of these DEGs was conducted by Cytoscape with STRING (Search Tool for the Retrieval of Interacting Genes). Our results showed that the expression trends of 21 up-regulated genes and 116 down-regulated were similar in selected three datasets. Analyzed by MCODE (Molecular Complex Detection) plug-in, 11 up-regulated and 16 down-regulated genes were selected. To further verify gene expression differences, GEPIA (Gene Expression Profiling Interactive Analysis) was implemented and we found 26 of 27 genes were found differently expressed in lung cancer compared with normal lung tissues. Furthermore, Kaplan–Meier analysis was used and we found 23 of 26 genes for overall survival indicated much less survival time. At last, three genes, CDH5, CLDN5, PECAM1, were found to be significantly decreased in lung cancer tissue proved through re-analysis of DAVID, which mainly co-related with leukocyte trans-endothelial migration. In conclusion, three significant down-regulated deferentially expressed genes with poor prognosis on lung cancer were identified basing on integrated bioinformatical methods. These down-expressed genes may be as a potential prognosis targets for patients with lung cancer.

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Discovering Biomarkers and Pathways Shared by Alzheimer’s Disease and Ischemic Stroke to Identify Novel Therapeutic Targets

Medicina ◽

10.3390/medicina55050191 ◽

2019 ◽

Vol 55 (5) ◽

pp. 191 ◽

Cited By ~ 12

Author(s):

Md. Rezanur Rahman ◽

Tania Islam ◽

Md. Shahjaman ◽

Toyfiquz Zaman ◽

Hossain Md. Faruquee ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Interaction Analysis ◽

Mapk Signaling ◽

Protein Protein Interactions ◽

Microarray Gene Expression ◽

Pathway Analyses

Background and objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein–protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein–drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.

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Identification of genes that influence poor prognosis in osteosarcoma

10.21203/rs.3.rs-506161/v1 ◽

2021 ◽

Author(s):

Yao Lu ◽

Yanli Li ◽

Xueliang Zeng ◽

Bei Li ◽

Qiongjun Xie ◽

...

Keyword(s):

Gene Expression ◽

Poor Prognosis ◽

Bone Cancer ◽

Interaction Network ◽

Venn Diagram ◽

Future Research ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Kaplan Meier ◽

Long Term Prognosis

Abstract Objectives Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. At present, the 5-year overall survival rate of OS patients is about 65%, and the long-term prognosis is still not ideal. The study was designed to screen genes that could contribute to the poor prognosis of OS and explore their potential pathogenic mechanisms. Methods The gene expression profile of the GSE94805 dataset from the GEO database, containing data from 12 U2OS cell samples, including four control, four quiescent, and four senescent samples was obtained. Co-expressed differentially expressed genes (DEGs) in OS U2OS cells were selected using the GEO2R tool and Venn diagram analysis. Next, using the STRING, Cytoscap, and Molecular Complex Detection (MCODE) plug-in, the related protein-protein interaction network among upregulated genes was analyzed. Moreover, Kaplan-Meier plots were used to analyze the relationship between the identified genes and OS prognosis. Genes significantly associated with worse prognosis were evaluated using the Gene Expression Profiling Interactive Analysis. Results Thirteen genes were confirmed to be significantly more expressed in OS than in normal tissues. Five genes (AURKB, EXO1, KIF4A, KIF15, and MCM4) were found to influence OS prognosis. Conclusion We identified five core genes related to the prognosis of OS and constructed a clinical prediction model for OS. Our data may provide a reference for future research on mechanisms, clinical diagnosis, and treatment of OS.

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