scholarly journals Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Kailun Zhou ◽  
Jincai Wu ◽  
Zhensheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Immune Cells ◽  
Prognostic Value ◽  
Tumor Tissue ◽  
Clinical Stage ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

scholarly journals Comprehensive Analysis of Clinical Prognosis and Molecular Immune Characterization of TPM4 in Pancreatic Cancer

2021 ◽  
Author(s):  
Xue Zhou ◽  
Xiaowei Zhu ◽  
Junchao Yao ◽  
Xue Wang ◽  
Ning Wang
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Positive Association ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinical Prognosis ◽  
Tumor Cell Migration

Abstract Pancreatic cancer (PC) is one of the most lethal human solid malignancies with devastating prognosis, making biomarker detection considerably important. Immune infiltrates in microenvironment is associated with patients’ survival in PC. The role of TPM4 (Tropomyosin 4) gene in PC has not been reported. Our study first identifies TPM4 expression and its potential biological functions in PC. The potential oncogenic roles of TPM4 was examined using the datasets of TCGA (The cancer genome atlas) and GEO (Gene expression omnibus). We investigated the clinical significance and prognostic value of TPM4 gene based on The Gene Expression Profiling Interactive Analysis (GEPIA) and survival analysis. TIMER and TISIDB databases were used to analyze the correlations between TPM4 gene and tumor-infiltrating immune cells. We found that the expression level of TPM4 was upregulated in PC malignant tissues with the corresponding normal tissues as controls. High TPM4 expression was correlated with the worse clinicopathological features and poor prognosis in PC cohorts. The positive association between TPM4 expression and tumor-infiltrating immune cells was identified in tumor microenvironment (TME). Moreover, functional enrichment analysis suggested that TPM4 might participate in cell adhesion and promote tumor cell migration. This is the first comprehensive study to disclose that TPM4 may serve as a novel prognostic biomarker associating with immune infiltrates and provide a potential therapeutic target for the treatment of PC.This study is not a clinical trial without the registration number.

scholarly journals Up-Regulation of MiR-330-5p is Associated with the Advanced Clinical Stage of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Hong-Yu Wei ◽  
He-Qing Huang ◽  
Gang Chen ◽  
Jie-Zhuang Huang ◽  
Yi-Wu Dang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Target Genes ◽  
Tumor Tissue ◽  
Characteristic Curve ◽  
Clinical Stage ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Summary Receiver Operating Characteristic ◽  
Advanced Clinical Stage ◽  
Standard Mean Difference

Abstract Purpose: A opposite expression levels and roles of miR-330-5p in hepatocellular carcinoma (HCC) have been reported in previous studies, so the clinicopathological implications and the prospective molecular mechanism of miR-330-5p in HCC require elaboration.Materials and methods: To examine the mRNA expression profile of hsa-miR-330-5p in HCC, a in-house RT-qPCR was performed, and the expression data was extracted from sequencing data and gene microarrays from the datasets of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), ONCOMINE, ArrayExpress, and Sequence Read Archive (SRA). To have an overview of the clinical value of miR-330-5p, all possible data were integrated to calculate the standard mean difference (SMD) and area under the curve (AUC) from summary receiver operating characteristic curve (sROC). The target genes of miR-330-5p were also predicted and the relative signaling pathways were investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.Results: According to the integrative analysis, the expression value of miR-330-5p in HCC was higher than in non-tumor tissue (SMD=0.47, 95CI%=0.31-0.63), and high expression of miR-330-5p was related with advanced HCC stage (SMD=0.27, 95CI%=0.08-0.46), poor differentiation (SMD=0.39, 95CI%=0.21-0.58) and poor prognosis (HR=1.91, Log-rank P=0.014). GLUD1, GOT1 and GLS2 were highly likely to be targeted by miR-330-5p, and the progression and prognosis of HCC might be influenced via the miR-330-5p–GLUD1/GOT1/GLS2 axis.Conclusion: The result showed a high-expressed level of miR-330-5p was in the HCC tissue compared with non-tumor tissue, and the overexpression of miR-330-5p indicated poor progression and prognosis in HCC.

scholarly journals Identification of RB1 Gene as a Immune-Related Prognostic Signature Based on Tumor Mutation Burdens in Bladder Cancer

2021 ◽  
Author(s):  
Ming Chen ◽  
Hong Cheng ◽  
Tiange Wu ◽  
Zeping Gui ◽  
Ying Gao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Poor Overall Survival ◽  
Mutant Type

Abstract Background: Bladder cancer (BC) is known as the eleventh most common malignant tumor all over the world, for either males or females. Developing effective regimens targeting more promising biomarkers aiming for better prognosis are required. Immune checkpoint inhibitors (ICI) have been demonstrated as a prospective and practical means to resist cancers. Theoretically, adequate infiltration of immune cells indicates more immunotherapy targets and may promise better prognosis.Methods: Full transcriptome data (n=433), clinical information (n=581) and mutation sequencing (n=412) were obtained freely from The Cancer Genome Atlas and independent mutation sequencing data of 101 samples were acquired from International Cancer Genome Consortium. Statistical processing was conducted using R packages with R x64 4.0.2. Gene biologically functional research was performed with gene set enrichment analysis (GSEA) based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 22 types of immune cell infiltration were assessed and calculated in 398 samples of BC tumors.Results: Tumor mutation burdens (TMB) of mutant type groups were higher than wild type groups for 19 genes, except for FGFR3 and CREBBP verifying that genomic mutation associates positively with TMB in BC tumor. Kaplan-Meier analysis showed high mutation frequency on RB1 had a negative effect on prognosis of BC patients and RB1 was an independent prognostic factor (p=0.004, HR=1.776) in BC. It was also demonstrated that RB1 mainly participate in singling pathways of cell proliferation and cell cycle. Proportions and correlation of 22 types of immune cells in 433 samples were determined. Immune cells with similar function are inclined to co-exist in tumor microenvironment of BC. Among them, regulatory T cells (Tregs) were detected as a negatively correlated type immune cell to mutation of RB1 that probably increases the incidence of tumor immune escaping in BC.Conclusion: RB1 can be identified as an independent prognostic predictor, and there is a chance for contribution to poor overall survival as the mutation occurs. What's more, mutation of RB1 also functions as a biomarker that represses the infiltration of Tregs, increasing the incidence of tumor immune escaping in BC.

scholarly journals Predictive value of m5C regulatory gene expression in pancreatic adenocarcinoma

2020 ◽  
Author(s):  
Xiao Yu ◽  
Qingyuan Zheng ◽  
Yabin Chen ◽  
Qiyao Zhang ◽  
Yuting He ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Adenocarcinoma ◽  
Regulatory Gene ◽  
Copy Number Variations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Patient Prognosis ◽  
Regulated Gene Expression

Abstract Background Pancreatic adenocarcinoma (PAAD) is the most malignant digestive tumor. The global incidence of pancreatic cancer has been rapidly trending upwards, necessitating an exploration of potential prognostic biomarkers and mechanisms of disease development. One of the most prevalent RNA modifications is 5-methylcytosine (m5C). However, its contribution to PAAD remains unclear. Methods Data from The Cancer Genome Atlas (TCGA) database, including genes, copy number variations (CNVs), and simple nucleotide variations (SNVs), were obtained in the present study to identify gene signatures and prognostic values for m5C regulators in PAAD. Results Regulatory gene m5C changes were significantly correlated with TP53, BRCA1, CDKN2A, and ATM genes, which play important roles in PAAD pathogenesis. In particular, there was a significant relationship between m5C regulatory gene CNVs, especially in genes encoding epigenetic “writers”. According to m5C-regulated gene expression in clinically graded cases, one m5C-regulated genes, DNMT3A, showed both a strong effect on CNVs and a significant correlation between expression level and clinical grade (p<0.05). Furthermore, low DNMT3A expression was not only associated with poor PAAD patient prognosis but also with the ribosomal processing. The relationship between low DNMT3A expression and poor prognosis was confirmed in an International Cancer Genome Consortium (ICGC) validation dataset. Conclusion m5C regulatory genes in pancreatic cancer patients that correlate with patient prognosis and survival

scholarly journals A TP53-associated immune prognostic signature for the prediction of the overall survival and therapeutic responses in pancreatic cancer

2021 ◽  
Vol 19 (1) ◽  
pp. 191-208
Author(s):  
Yi Liu ◽  
◽  
Long Cheng ◽  
Xiangyang Song ◽  
Chao Li ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Tp53 Mutation ◽  
Prolactin Receptor ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Large Sample Size ◽  
Sequencing Data

<abstract> <p>Pancreatic cancer (PC) is a highly fatal disease correlated with an inferior prognosis. The tumor protein p53 (TP53) is one of the frequent mutant genes in PC and has been implicated in prognosis. We collected somatic mutation data, RNA sequencing data, and clinical information of PC samples in the Cancer Genome Atlas (TCGA) database. TP53 mutation was an independent prognostic predictor of PC patients. According to TP53 status, Gene set enrichment analysis (GSEA) suggested that TP53 mutations were related to the immunophenotype of pancreatic cancer. We identified 102 differentially expressed immune genes (DEIGs) based on TP53 mutation status and developed a TP53-associated immune prognostic model (TIPM), including Epiregulin (EREG) and Prolactin receptor (PRLR). TIPM identified the high-risk group with poor outcomes and more significant response potential to cisplatin, gemcitabine, and paclitaxel therapies. And we verified the TIPM in the International Cancer Genome Consortium (ICGC) cohort (PACA-AU) and Gene Expression Omnibus (GEO) cohort (GSE78229 and GSE28735). Finally, we developed a nomogram that reliably predicts overall survival in PC patients on the bias of TIPM and other clinicopathological factors. Our study indicates that the TIPM derived from TP53 mutation patterns might be an underlying prognostic therapeutic target. But more comprehensive researches with a large sample size is necessary to confirm the potential.</p> </abstract>

scholarly journals An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 716
Author(s):  
Grasieli de Oliveira ◽  
Paula Paccielli Freire ◽  
Sarah Santiloni Cury ◽  
Diogo de Moraes ◽  
Jakeline Santos Oliveira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Prognostic Value ◽  
Meta Analysis ◽  
The Cancer Genome Atlas ◽  
Secreted Proteins ◽  
Ductal Adenocarcinoma ◽  
Public Data ◽  
Potential Biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

scholarly journals Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guoliang Jia ◽  
Zheyu Song ◽  
Zhonghang Xu ◽  
Youmao Tao ◽  
Yuanyu Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prognosis ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals Prognostic value of CLIC3 mRNA overexpression in bladder cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8348
Author(s):  
Mei Chen ◽  
Shufang Zhang ◽  
Xiaohong Wen ◽  
Hui Cao ◽  
Yuanhui Gao
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Mrna Expression ◽  
Prognostic Value ◽  
Multivariate Analyses ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Normal Tissues

Background Human intracellular chloride channel 3 (CLIC3) is involved in the development of various cancers, but the expression and prognostic value of CLIC3 mRNA in bladder cancer (BC) remain unclear. Methods The gene expression data and clinical information of CLIC3 were obtained from the Gene Expression Omnibus (GEO) database and verified in the Oncomine and The Cancer Genome Atlas (TCGA) database. The expression of CLIC3 mRNA in BC tissues and adjacent normal tissues was detected by quantitative real-time polymerase chain reaction (qRT-PCR). The Kaplan-Meier method was used to analyze the relationship between the expression of CLIC3 mRNA and the prognosis of BC. Cox univariate and multivariate analyses were performed on the overall survival and tumor-specific survival of BC patients. The genes coexpressed with CLIC3 were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). CLIC3-related signal transduction pathways in BC were explored with gene set enrichment analysis (GSEA). Results The expression of CLIC3 mRNA in BC tissues was higher than that in normal tissues (P < 0.01). High CLIC3 mRNA expression was associated with age (P = 0.021) and grade (P = 0.045) in BC patients. High CLIC3 mRNA expression predicted a poor prognosis in BC patients (P < 0.05). Cox univariate and multivariate analyses showed that high CLIC3 mRNA expression was associated with tumor-specific survival in BC patients (P < 0.05). Functional enrichment analyses indicated that CLIC3 may be significantly associated with the cell cycle, focal adhesion, the extracellular matrix (ECM) receptor interaction and the P53 signaling pathway. Conclusions CLIC3 mRNA is highly expressed in BC, and its high expression is related to the adverse clinicopathological factors and prognosis of BC patients. CLIC3 can be used as a biomarker for the prognosis of BC patients.

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