scholarly journals Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi Wang ◽  
Lei Tu ◽  
Minfeng Chen ◽  
Shiyu Tong
Keyword(s):  
Bladder Cancer ◽  
Tumor Microenvironment ◽  
Predictive Model ◽  
Cell Lines ◽  
Cox Regression ◽  
Mrna Level ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Roc Curve Analysis ◽  
Qrt Pcr

Abstract Background Accumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA. Methods We employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines. Results 365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines. Conclusions Our study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.

scholarly journals Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolin Yu ◽  
Xiaomei Zhang ◽  
Yanxia Zhang
Keyword(s):  
Tumor Microenvironment ◽  
Confidence Interval ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Lasso Regression ◽  
The Status

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

scholarly journals A Three Protein-Coding Gene Prognostic Model Predicts Overall Survival in Bladder Cancer Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiang-hui Ning ◽  
Yuan-yuan Qi ◽  
Fang-xin Wang ◽  
Song-chao Li ◽  
Zhan-kui Jia ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Zinc Finger Protein ◽  
Risk Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Lasso Regression ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Differentially Methylated Genes

Bladder cancer (BLCA) is the most common urinary tract tumor and is the 11th most malignant cancer worldwide. With the development of in-depth multisystem sequencing, an increasing number of prognostic molecular markers have been identified. In this study, we focused on the role of protein-coding gene methylation in the prognosis of BLCA. We downloaded BLCA clinical and methylation data from The Cancer Genome Atlas (TCGA) database and used this information to identify differentially methylated genes and construct a survival model using lasso regression. We assessed 365 cases, with complete information regarding survival status, survival time longer than 30 days, age, gender, and tumor characteristics (grade, stage, T, M, N), in our study. We identified 353 differentially methylated genes, including 50 hypomethylated genes and 303 hypermethylated genes. After annotation, a total of 227 genes were differentially expressed. Of these, 165 were protein-coding genes. Three genes (zinc finger protein 382 (ZNF382), galanin receptor 1 (GALR1), and structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1)) were selected for the final risk model. Patients with higher-risk scores represent poorer survival than patients with lower-risk scores in the training set ( HR = 2.37 , 95% CI 1.43-3.94, p = 0.001 ), in the testing group ( HR = 1.85 , 95% CI 1.16-2.94, p = 0.01 ), and in the total cohort ( HR = 2.06 , 95% CI 1.46-2.90, p < 0.001 ). Further univariate and multivariate analyses using the Cox regression method were conducted in these three groups, respectively. All the results indicated that risk score was an independent risk factor for BLCA. Our study screened the different methylation protein-coding genes in the BLCA tissues and constructed a robust risk model for predicting the outcome of BLCA patients. Moreover, these three genes may function in the mechanism of development and progression of BLCA, which should be fully clarified in the future.

scholarly journals m6A RNA Methylation Regulators Impact Prognosis and Tumor Microenvironment in Renal Papillary Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lianze Chen ◽  
Baohui Hu ◽  
Xinyue Song ◽  
Lin Wang ◽  
Mingyi Ju ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Patterns ◽  
The Cancer Genome Atlas ◽  
Rna Modification ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Rna Methylation

Accumulating evidence has proven that N6-methyladenosine (m6A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m6A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m6A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m6A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m6A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m6A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m6A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m6A RNA modification in PRCC and provide essential guidance for therapeutic strategies.

scholarly journals Stromal LAG-3+cells infiltration defines poor prognosis subtype muscle-invasive bladder cancer with immunoevasive contexture

2020 ◽  
Vol 8 (1) ◽  
pp. e000651 ◽  
Author(s):  
Han Zeng ◽  
Quan Zhou ◽  
Zewei Wang ◽  
Hongyu Zhang ◽  
Zhaopei Liu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
T Cells ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Mrna Level ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive

BackgroundLymphocyte activation gene 3 (LAG-3) is a promising immune checkpoint therapeutic target being evaluated in clinical trials. We assessed the LAG-3+cells distribution, its association with clinical outcomes and immune contexture and its role in the landscape of muscle-invasive bladder cancer (MIBC) treatment.Methods141 patients with MIBC from Zhongshan Hospital were included for survival and adjuvant chemotherapy (ACT) benefit analyses. 32 fresh resected samples of MIBC were collected to detect CD8+T cells functional state. The molecular classification analyses were based on 391 patients with MIBC from The Cancer Genome Atlas. Immunohistochemistry and flow cytometry were performed to characterize various immune cells infiltration.ResultsIn Kaplan-Meier analyses and Cox regression models, stromal LAG-3+cells enrichment was consistently associated with inferior overall survival and disease-free survival, and indicated suboptimal responsiveness to ACT. Patents with high stromal LAG-3+cells possessed increased protumor cells, immunosuppressive cytokines and immune checkpoint expression. The phenotypic analyses of CD8+T cells correlated its dysfunctional state with LAG-3+cells. Besides, LAG-3 mRNA level was linked to luminal and basal subtypes of MIBC. LAG-3-high tumors exhibited limited FGFR3 mutation and signaling signature, and displayed activated immunotherapeutic and EGFR-associated pathway.ConclusionsStromal LAG-3+cells abundance indicated an immunoevasive contexture with dysfunctional CD8+T cells, and represented an independent predictor for adverse survival outcome and ACT resistance in MIBC. LAG-3 expression could potentially be a novel biomarker for FGFR3-targeted and EGFR-targeted therapies and immunotherapy. The crucial role of LAG-3+cells in the therapeutic landscape of MIBC needs further validation retrospectively and prospectively.

scholarly journals Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Rujia Qin ◽  
Wen Peng ◽  
Xuemin Wang ◽  
Chunyan Li ◽  
Yan Xi ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Lasso Regression ◽  
Hub Genes ◽  
Dynamic Regulation ◽  
Cox Regression Analysis

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.

scholarly journals Prognostic Value of Immune-Related lncRNA Pairs In Patients with Bladder Cancer

2021 ◽  
Author(s):  
zhenzhen Gao ◽  
Dongjuan Wu ◽  
Wenwen Zheng ◽  
taohong Zhu ◽  
Ting Sun ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Model ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Efficacy Evaluation ◽  
Specific Expression

Abstract Background: The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific expression level, have important implications for the prognosis of patients with bladder cancer. Methods: Based on The Cancer Genome Atlas (TCGA) database, we downloaded original transcript data, obtained the ir-lncRNAs using a coexpression method, and identified the differentially expressed pairs of ir-lncRNAs (DE-ir-lncRNAs) using univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves (ROC) were generated; the area under the curve was calculated; the Akaike information criterion (AIC) of the 5-y ROC was determined; the optimal cutoff value of the high- and low-risk populations of patients with bladder cancer was confirmed, and the optimal risk model was established. The clinical value of the model was verified using survival analysis, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation. Results: In total, 49 pairs of DE-ir-lncRNAs were identified, and 21 pairs were included in the Cox regression model. In this study, ir-lncRNA pairs were obtained, and a risk regression model was established on the premise of not involving the specific expression value of transcripts. Conclusions: The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.

scholarly journals A Robust Hypoxia Risk Score Predicts the Clinical Outcomes and Tumor Microenvironment Immune Characters in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhi Liu ◽  
Qiao Tang ◽  
Tiezheng Qi ◽  
Belaydi Othmane ◽  
Zhe Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cancer Immunotherapy ◽  
Clinical Outcomes ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Unmet Need ◽  
The Cancer Genome Atlas

BackgroundBladder cancer (BLCA) is one of the most common urinary malignancies with poor prognosis. There is an unmet need to develop novel robust tools to predict prognosis and treatment efficacy for BLCA.MethodsThe hypoxia-related genes were collected from the Molecular Signatures Database. The TCGA-BLCA cohort was downloaded from the Cancer Genome Atlas and then was randomly divided into training and internal validation sets. Two external validation cohorts were gathered from Gene Expression Omnibus. Also, another independent validation cohort (Xiangya cohort) was collected from our hospital. The Cox regression model with the LASSO algorithm was applied to develop the hypoxia risk score. Then, we correlated the hypoxia risk score with the clinical outcomes, the tumor microenvironment (TME) immune characteristics, and the efficacy prediction for several treatments, which included cancer immunotherapy, chemotherapy, radiotherapy, and targeted therapies.ResultsHypoxia risk score was an independent prognostic factor. A high-risk score indicated an inflamed TME based on the evidence that hypoxia risk score positively correlated with the activities of several cancer immunity cycles and the infiltration levels of many tumor-infiltrating immune cells, such as CD8 + T cells, Dendritic cells, and NK cells. Consistently, the hypoxia risk score was positively related to the expression of several immune checkpoints, such as PD-L1, PD-1, CTLA-4, and LAG-3, as well as the T cell inflamed score. Furthermore, the hypoxia risk score positively correlated with the enrichment scores of most immunotherapy-positive gene signatures. Therefore, patients with higher risk score may be more sensitive to cancer immunotherapy. Meanwhile, the hypoxia risk score was positively related to the sensitivities of several chemotherapeutic drugs, including Cisplatin, Docetaxel, Paclitaxel, Bleomycin, Camptothecin, and Vinblastine. Similarly, the enrichment scores for radiotherapy-predicted pathways and EGFR ligands were higher in the high-risk score group. Conversely, the enrichment scores of several immunosuppressive oncogenic pathways were significantly higher in the low-risk score group, such as the WNT-β-catenin network, PPARG network, and FGFR3 network.ConclusionsWe developed and validated a new hypoxia risk score, which could predict the clinical outcomes and the TME immune characteristics of BLCA. In general, the hypoxia risk score may aid in the precision medicine for BLCA.

scholarly journals M6A Classification Combined With Tumor Microenvironment Immune Characteristics Analysis of Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huili Zhu ◽  
Xiaocan Jia ◽  
Yuping Wang ◽  
Zhijuan Song ◽  
Nana Wang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Cox Regression ◽  
Principal Component ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Clinical Benefits

BackgroundStudies have shown that N6-methyl adenosine (m6A) plays an important role in cancer progression; however, the underlying mechanism of m6A modification in tumor microenvironment (TME) cell infiltration of bladder cancer remains unclear. This study aimed to investigate the role of m6A modification in TME cell infiltration of bladder cancer.MethodsThe RNA expression profile and clinical data of bladder cancer were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We assessed the m6A modification patterns of 664 bladder cancer samples based on 20 m6A regulators through unsupervised clustering analysis and systematically linked m6A modification patterns to TME cell infiltration characteristics. Gene ontology and gene set variation analyses were conducted to analyze the underlying mechanism based on the assessment of m6A methylation regulators. Principal component analysis was used to construct the m6A score to quantify m6A modification patterns of bladder cancer.ResultsThe genetic and expression alterations in m6A regulators were highly heterogeneous between normal and bladder tissues. Three m6A modification patterns were identified. The cell infiltration characteristics were highly consistent with the three immune phenotypes, including immune rejection, immune inflammation, and immune desert. The biological functions of three m6A modification patterns were different. Cox regression analyses revealed that the m6A score was an independent signature with patient prognosis (HR = 1.198, 95% CI: 1.031–1.390). Patients with a low-m6A score were characterized by increased tumor mutation burden, PD-L1 expression, and poorer survival. Patients in the low-m6A score group also showed significant immune responses and clinical benefits in the CTLA-4 immunotherapy cohort (p =0.0069).ConclusionsThe m6A methylation modification was related to the formation of TME heterogeneity and complexity. Assessing the m6A modification pattern of individual bladder cancer will improve the understanding of TME infiltration characteristics.

scholarly journals Development and Validation of a Six-Gene Prognostic Signature for Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Fei Xu ◽  
Qianqian Tang ◽  
Yejinpeng Wang ◽  
Gang Wang ◽  
Kaiyu Qian ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Cell Lines ◽  
Cox Regression ◽  
Gene Signature ◽  
Prognostic Signature ◽  
Cancer Specific Survival ◽  
Qrt Pcr ◽  
Novel Biomarkers ◽  
Six Genes

Human bladder cancer (BCa) is the most common urogenital system malignancy. Patients with BCa have limited treatment efficacy in clinical practice. Novel biomarkers could provide more crucial information conferring to cancer diagnosis, treatment, and prognosis. Here, we aimed to explore and identify novel biomarkers associated with cancer-specific survival of patients with BCa to build a prognostic signature. Based on univariate Cox regression, Lasso regression, and multivariate Cox regression analysis, we conducted an integrated analysis in the training set (GSE32894) and established a six-gene signature to predict the cancer-specific survival for human BCa. The six genes were Cyclin Dependent Kinase 4 (CDK4), E2F Transcription Factor 7 (E2F7), Collagen Type XI Alpha 1 Chain (COL11A1), Bradykinin Receptor B2 (BDKRB2), Yip1 Interacting Factor Homolog B (YIF1B), and Zinc Finger Protein 415 (ZNF415). Then, we validated the prognostic value of the model by using two other datasets (GSE13507 and TCGA). Also, we conducted univariate and multivariate Cox regression analyses, and results indicated that the six-gene signature was an independent prognostic factor of cancer-specific survival of patients with BCa. Functional analysis was performed based on the differentially expressed genes of low- and high-risk patients, and we found that they were enriched in lipid metabolic and cell division-related biological processes. Meanwhile, the gene set enrichment analysis (GSEA) revealed that high-risk samples were enriched in cell cycle and cancer-related pathways [G2/M checkpoint, E2F targets, mitotic spindle, mTOR signaling, spermatogenesis, epithelial–mesenchymal transition (EMT), DNA repair, PI3K/AKT/mTOR signaling, unfolded protein response (UPR), and MYC targets V2]. Lastly, we detected the relative expression of each signature in BCa cell lines by quantitative real-time PCR (qRT-PCR). As far as we know, currently, the present study is the first research that developed and validated a cancer-specific survival prognostic index based on three independent cohorts. The results revealed that this six-gene signature has a predictive ability for cancer-specific prognosis. Moreover, we also verified the relative expression of these six signatures between the bladder cell line and four BCa cell lines by qRT-PCR. Nevertheless, experiments to further explore the function of six genes are lacking.

scholarly journals Prognostic value of immune-related lncRNA pairs in patients with bladder cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhenzhen Gao ◽  
Dongjuan Wu ◽  
Wenwen Zheng ◽  
Taohong Zhu ◽  
Ting Sun ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Model ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Efficacy Evaluation ◽  
Specific Expression

Abstract Background The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer. Methods Based on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation. Results In total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts. Conclusions The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.

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