scholarly journals Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients

2019 ◽  
Vol 26 (1) ◽  
pp. 107327481985511 ◽  
Author(s):  
Qi Wang ◽  
Zongze He ◽  
Yong Chen
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Homologous Recombination ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Primary Therapy

Low-grade gliomas (LGGs) are a highly heterogeneous group of slow-growing, lethal, diffusive brain tumors. Temozolomide (TMZ) is a frequently used primary chemotherapeutic agent for LGGs. Currently there is no consensus as to the optimal biomarkers to predict the efficacy of TMZ, which calls for decision-making for each patient while considering molecular profiles. Low-grade glioma data sets were retrieved from The Cancer Genome Atlas. Cox regression and survival analyses were applied to identify clinical features significantly associated with survival. Subsequently, Ordinal logistic regression, co-expression, and Cox regression analyses were applied to identify genes that correlate significantly with response rate, disease-free survival, and overall survival of patients receiving TMZ as primary therapy. Finally, gene expression and methylation analyses were exploited to explain the mechanism between these gene expression and TMZ efficacy in LGG patients. Overall survival was significantly correlated with age, Karnofsky Performance Status score, and histological grade, but not with IDH1 mutation status. Using 3 distinct efficacy end points, regression and co-expression analyses further identified a novel 4-gene signature of ASPM, CCNB1, EXO1, and KIF23 which negatively correlated with response to TMZ therapy. In addition, expression of the 4-gene signature was associated with those of genes involved in homologous recombination. Finally, expression and methylation profiling identified a largely unknown olfactory receptor OR51F2 as potential mediator of the roles of the 4-gene signature in reducing TMZ efficacy. Taken together, these findings propose the 4-gene signature as a novel panel of efficacy predictors of TMZ therapy, as well as potential downstream mechanisms, including homologous recombination, OR51F2, and DNA methylation independent of MGMT.

scholarly journals A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

2021 ◽  
Vol 20 ◽  
pp. 153303382199208
Author(s):  
Wentao Liu ◽  
Jiaxuan Zou ◽  
Rijun Ren ◽  
Jingping Liu ◽  
Gentang Zhang ◽  
...  
Keyword(s):  
Risk Score ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Normal Brain ◽  
Cox Regression Analysis ◽  
Genome Atlas

Aim: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. Methods: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. Results: We identified a total of 1489 genes significantly correlated with patients’ prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. Conclusions: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

scholarly journals Identification of a 9-Gene Prognostic signature for breast cancer

2020 ◽  
Author(s):  
Zelin Tian ◽  
Jianing Tang ◽  
Xing Liao ◽  
Qian Yang ◽  
Yumin Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Breast cancer (BRCA) is the most common cancer among women worldwide and results in the second leading cause of woman cancer death.Methods This study sought to develop a prognostic gene signature to predict the prognosis of patients with BRCA. Studies were performed using the genome-wide data of BRCA patients from the Gene Expression Omnibus dataset (GSE20685, GSE42568, GSE20711, GSE88770). Univariate COX regression analysis was used to determine the association between gene expression levels and overall survival(OS) in each dataset. Taking P value < 0.05 as the inclusion criterion, the common genes in all datasets were selected as prognostic genes, and a 9-gene prognostic signature was developed.Results The Kaplan-Meier survival curve was constructed using log-rank test to assess survival differences. The overall survival of patients in the low-risk group was significantly higher than that in the high-risk group. ROC analysis showed that this 9-gene signature showed good diagnostic efficiency both in overall survival(OS) and disease free survival(DFS). The 9-gene signature was further validated using GSE16446 dataset. In addition, multiple Cox regression analysis showed that this 9-gene signature was an independent risk factor. Finally, we established a nomogram that integrates conventional clinicopathological features and 9-gene signature. The analysis of the calibration plots showed that the nomogram has good performance.Conclusions This study has developed a reliable 9-gene prognostic signature, which is of great value in predicting the prognosis of BRCA and will help to make personalized treatment decisions for patients at different risk score.

scholarly journals Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Feng Jiang ◽  
Chuyan Wu ◽  
Ming Wang ◽  
Ke Wei ◽  
Jimei Wang
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Single Gene ◽  
Prediction Method ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Models ◽  
Normal Tissues ◽  
Predictive Risk

AbstractOne of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

Development and validation of an unfolded protein response-related gene signature to predict overall survival in HNSCC.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18033-e18033
Author(s):  
Jun Chen ◽  
Bei Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Unfolded Protein Response ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Related Gene ◽  
Hallmarks Of Cancer ◽  
Unfolded Protein ◽  
Protein Response

e18033 Background: Genomic expression profiles have enabled the classification of head and neck squamous cell carcinoma (HNSCC) into molecular sub-types and provide prognostic information, which have implications for the personalized treatment of HNSCC beyond clinical and pathological features. Methods: Gene-expression profiling was identified in TCGA- HNSCC (n = 492) and validated with the Gene Expression Ominibus (GEO) dataset(n = 270) for which RNA sequencing data and clinical covariates were available. A single-sample gene set enrichment analysis (ssGSEA) algorithm were used to quantified the levels of various hallmarks of cancer. And LASSO Cox regression model was used to screen robust prognostic biomarkers to identify the best set of survival-associated gene signatures in HNSCC. Statistical analyses were performed using R version 3.4.4. Results: We identified unfolded protein response as the primary risk factor for survival（cox coefficient = 17.4 [8.4-26.3], P < 0.001）among various hallmarks of cancer in TCGA- HNSCC. And unfolded protein response ssGESA scores were significantly elevated in patients who died during follow up (P = 0.009). Kaplan-Meier analysis showed that patients with low ssGSEA scores of unfolded protein response exhibited better OS (HR = 0.69, P = 0.008). And we established an unfolded protein response-related gene signature based on lasso cox. We then apply the unfolded protein response -related gene signature to classify patients into the high risk group and the low risk group with the cutoff of 0.18. Adjusted for stage，age，gender, our signature was an independent risk factor for overall survival in TCGA cohorts (HR = 0.39 [0.28-0.53],P = < 0.001). In external independent cohorts, similar results were observed. In the validation cohort GEO65858, the patients with high unfolded protein response score showed longer survival (HR = 0.62 [0.38-1.0], P = 0.049). And adjusted for stage，age，HPV state, the multivariate cox regression analysis showed that unfolded protein response-related gene signature exhibited an independent risk prediction for overall survival in 270 patients with HNSCC (HR = 0.57 [0.35-0.94], P = 0.026). Conclusions: By analyzing the gene-expression data with bioinformation approach, we developed and validated a risk prediction model with unfolded protein response -related expression scores in HNSCC, which have the potential to identify patients who could have better overall survival.

scholarly journals A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Zhanzhong Ma ◽  
Wenli Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Clinical Practice ◽  
Correlation Coefficient ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Cancer Genome ◽  
Survival Prediction

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

scholarly journals Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yin Qiu Tan ◽  
Yun Tao Li ◽  
Teng Feng Yan ◽  
Yang Xu ◽  
Bao Hui Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Survival Rates ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Sequencing Analysis ◽  
Six Genes ◽  
Genome Atlas ◽  
Overall Survival Rates

BackgroundThe immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study.MethodsWe collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein–protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients’ overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA).ResultsSix genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG.ConclusionJoint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).

scholarly journals Development and validation of a novel ten-gene signature predicting prognosis in Hepatocellular carcinoma

2020 ◽  
Author(s):  
Guanbao Zhou ◽  
Genjie Lu ◽  
Liang Yang ◽  
Yangfang Lu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hierarchical Clustering ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Molecular Biomarkers ◽  
The Cancer Genome Atlas ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with relatively poor prognosis. Thus, we aimed to identify novel molecular biomarkers to effectively predict the prognosis of HCC patients and eventually guide treatment. Methods: Prognosis-associated genes were determined by Kaplan-Meier and multivariate Cox regression analyses using the expression and clinical data of 373 HCC patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. The classification of AML was performed by unsupervised hierarchical clustering of ten gene expression levels. A prognostic risk score was established based on a linear combination of ten gene expression levels using the regression coefficients derived from the multivariate Cox regression models. Results: A total of 183 genes were significantly associated with prognosis in HCC. SLC25A15, RAB8A, GOT2, SORBS2, IL18RAP were top five protective genes, while FHL3, AMD1, DCAF13, UBE2E1, PTDSS2 were top five risk genes in HCC. SLC25A15, GOT2, IL18RAP were significantly down-regulated and DCAF13, PTDSS2 and SORBS2 were significantly up-regulated in the HCC samples and these genes exhibited high accuracy in differentiating HCC tissues from normal liver tissues. Hierarchical clustering analysis of the ten genes discovered three clusters of HCC patients. HCC tumors of cluster1 and 2 were significantly associated with more favourable OS than those of cluster3, cluster2 tumors showed higher pathologic stage than cluster3 tumors. The risk score was predictive of increased mortality rate in HCC patients. Conclusions: The ten-gene signature and the risk score may turn out to be novel molecular biomarkers and stratification of HCC patients to considerably ameliorate the prognostic prediction.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

scholarly journals Serine Incorporator 2 (SERINC2) Expression Predicts an Unfavorable Prognosis of Low-Grade Glioma (LGG): Evidence from Bioinformatics Analysis

2020 ◽  
Vol 70 (10) ◽  
pp. 1521-1532
Author(s):  
Chunxiao Qi ◽  
Lei Lei ◽  
Jinqu Hu ◽  
Gang Wang ◽  
Jiyuan Liu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Membrane Lipids ◽  
Transmembrane Protein ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Normal Brain ◽  
Hub Genes ◽  
Cox Regression Analysis

Abstract Serine Incorporator 2 (SERINC2) is a transmembrane protein that incorporates serine into membrane lipids. The function of SERINC2 in tumors has been reported, but the role of SERINC2 in gliomas is not fully understood. RNA-sequencing data from The Cancer Genome Atlas (TCGA) (530 cases of low-grade glioma (LGG) and 173 cases of glioblastoma multiforme (GBM)) and microarray data from Gene Expression Omnibus (GEO) (Accession No. GSE16011, 284 cases gliomas were included) were acquired. Bioinformatics analysis was performed as the primary method to examine the function of SERINC2 and its correlated genes in glioma. SERINC2 was highly expressed in GBM compared with LGG and normal brain tissues. Elevated SERINC2 expression predicted shorter 5-, 10-, and 15-year overall survival (OS) of LGG patients and isocitrate dehydrogenase-1 (IDH-1) mutation-type LGG patients but had no effect on the OS of GBM patients. Cox regression analysis showed that SERINC2 was an independent factor in LGG OS. Methylation analysis found that 13 CpG methylation sites (methylation450k) correlated with SERINC2 expression in LGG. The mRNA expression level of SERINC2 was significant lower in the DNA deletion group than in the intact and amplification groups. A total of 390 copositive and 244 conegative correlation genes with SERINC2 were obtained from LGG in TCGA-LGG and GSE16011. Gene ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the copositive correlation genes were primarily enriched in the mitotic process and cell cycle. Combining the results from the protein-protein interaction (PPI) network of SERINC2 correlation genes and CytoHubba led to the selection of 10 hub genes (CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE). OncoLnc analysis confirmed that high expression levels of these hub genes were associated with poor OS in LGG. Our results suggested that aberrant SERINC2 expression existed in glioma and that its expression might be a potential prognostic marker in LGG patients. CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE may be potential biomarkers and therapeutic targets for LGG.

scholarly journals PRINS lncRNA Is a New Biomarker Candidate for HPV Infection and Prognosis of Head and Neck Squamous Cell Carcinomas

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 762
Author(s):  
Magda Kopczyńska ◽  
Tomasz Kolenda ◽  
Kacper Guglas ◽  
Joanna Sobocińska ◽  
Anna Teresiak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Head And Neck ◽  
Squamous Cell ◽  
Disease Free Survival ◽  
Hpv Infection ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Potential Biomarker

Numerous studies have shown that human papillomavirus (HPV) infection is one of the important risk factors for head and neck squamous cell carcinoma (HNSCC) progression and affects the expression of multiple genes, which might serve as new biomarkers. This study examines the effects of HPV infection on long non-coding RNA (lncRNA) expression and the immune system, particularly PRINS (Psoriasis susceptibility-related RNA Gene Induced by Stress). The Cancer Genome Atlas (TCGA) expression data for lncRNA genes and clinical data were analyzed by GraphPad Prism 5/7. The expressions of PRINS, CDKN2B-AS1, TTTY14, TTTY15, MEG3, and H19 were significantly different in HPV-positive and HPV-negative patients. HPV-positive patients with high PRINS expression demonstrated significantly better overall survival (OS) and disease-free survival (DFS). HPV-positive patients with high PRINS expression showed changes in gene expression associated with immune and antiviral responses. A majority of HPV-positive patients with high PRINS expression demonstrated a high number of immune cells within tumors. PRINS expression was significantly associated with HPV-infection HNSCC tumors. Validation of these results using data set from Gene Expression Omnibus (GEO) indicated that PRINS is upregulated in HPV active infections and in “atypical 1 (IR)” HNSCC clusters, negatively influencing patients’ overall survival. Patients with high PRINS expression display different immunological profiles than those with low expression levels. For instance, they have active HPV infection status or are clustered in the “atypical 1 (IR)” subtype of HNSCC which influences both viral infection and patients’ survival. It is likely that PRINS could be used as a potential biomarker for HNSCC patients, but its role is dual. On the one hand, it stimulates patients’ immune response, while on the other it can be favorable in virus replication.

Sign in / Sign up

Export Citation Format

Share Document