Cancer Translational Medicine

Original Research | Open Access

Vol.8 (2022) | Issue-4 | Page No: 151-161


Quercetin Inhibits Glioma Proliferation by Targeting CDK1 and CCNB1 - Bioinformatics and Network Pharmacology

Huaixu Li1#, Peng Gao1#, Haotian Tian1, Zhenyu Han2, Xingliang Dai1*, Hongwei Cheng1*


1. Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China

2. Department of Clinical Medicine, The First Clinical College of Anhui Medical University, Hefei, Anhui, China

#These authors contributed equally to this work

* Corresponding Author

Address for correspondence: Hongwei Cheng, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Shushan District, Hefei 230022, Anhui, China. E-mail:

Xingliang Dai, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Shushan District, Hefei 230022, Anhui, China. E-mail: 

Important Dates  

Date of Submission:   07-Nov-2022

Date of Acceptance:   16-Dec-2022

Date of Publication:   30-Dec-2022


Aim: To explore the targets and active ingredients of traditional Chinese medicine (TCM) that affect the malignant progression of glioma through bioinformatics and TCM network pharmacology analysis.

Methods: Differentially expressed genes in glioma and adjacent tissues were searched by using GSE35493, GSE50161, and GSE104291 databases in GEO database. GO and KEGG enrichment analysis were used to discover the potential mechanism of target genes. Hub genes were obtained using CytoHubba software package in Cytoscape. The active components and related targets of pulsatillie radix, radix salvia, capsellae herba, hippophae fractus, and ginkgo folium were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the target differentially expressed genes (DEGs) were applied to construct PPI network using Bisogenet package of Cytoscape software. Potential therapeutic target genes were obtained by comparing hub genes and the potential target genes corresponding to key components.

Results: Twenty-two potential target genes and 31 key components were screened to construct a compound-target network. PPI network and topology analysis identified two therapeutic target genes: CDK1 and CCNB1. The molecular docking results showed that quercetin has high affinity to CDK1 and CCNB1.

Conclusion: Quercetin may inhibit glioma proliferation and affect malignant progression by targeting CDK1 and CCNB1.

Keywords: Traditional Chinese medicine, glioma, network pharmacology, proliferation, therapeutic targets


Glioma is the most common primary malignant tumor of the central nervous system in adults, and it is a major health problem in the world nowadays.[1] The World Health Organization (WHO) divides gliomas into four grades according to the degree of malignancy. Grade I-II gliomas are considered low-grade gliomas (LGGs), and grade III-IV gliomas are called high-grade gliomas (HGGs).[2] Malignant glioma has a very high mortality rate, and the 5-year survival rate is less than 10%.[3] Traditional treatment methods include surgery, chemotherapy, and radiotherapy. At present, the most common treatment strategy for patients with glioma is surgical resection combined with radiotherapy and adjuvant chemotherapy, but its prognostic improvement for patients with glioma is limited.[4],[5] Therefore, further study of the potential molecular mechanisms of the malignant proliferation of gliomas and the development of new therapeutic targets will help improve the prognosis of patients and increase the survival rate.

Network pharmacology is a branch of pharmacology, which uses the network methods to analyze "multi-target, multi-component, and multi-channel" synergistic relationships between drugs, diseases, and targets. It has the advantages of systematization, integrity, and emphasis on interactions between drugs.[6] Studies have shown that network pharmacology analysis is applied to a variety of cancers, including acute myeloid leukemia (AML),[7] pancreatic cancer,[8] triple-negative breast cancer,[9] osteosarcoma,[10] and so on. It is reported in the literature that pulsatilliae radix (baitouweng in Chinese),[11] radix salvia (danshen in Chinese),[12] capsellae herba (jicai in Chinese),[13] hippophae fructus (shaji in Chinese),[14] and ginkgo folium (yinxingye in Chinese)[15] are all Chinese herbal medicines with anti-cancer effects. Some Chinese medicinal compounds have been reported to be used in tumor treatment or adjuvant therapy,[16],[17] and have even been shown to play an important role in the treatment of many diseases, such as hepatocellular carcinoma, breast cancer, colon cancer, etc.[8],[9],[20] However, there is literature on research related to Chinese medicine and glioma. The study of potential therapeutic targets of traditional Chinese medicine (TCM) for glioma and the exploration of effective Chinese medicine ingredients are expected to improve the therapeutic effect and improve the prognosis of patients with glioma.

This study was based on the Gene Expression Omnibus (GEO) database ( to analyze differentially expressed genes in gliomas through bioinformatics, conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analysis, and obtain hub genes through Cytoscape. By using the network pharmacology method, the active components and related targets of five Chinese medicines were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the compound target network was constructed ( Hub genes and the potential target genes corresponding to the key components to obtain potential therapeutic target genes were compared. The effective components of TCM in the treatment of glioma and its potential therapeutic targets to lay a foundation for the development of new glioma treatment strategies have been explored.


Data acquisition

The GEO database is a database that stores microarrays, next-generation sequencing, and other high-throughput sequencing data. Using this database, we can retrieve some experimental sequencing data uploaded by other people. We selected 3 datasets, namely, GSE35493, GSE50161, and GSE104291. Their dataset platforms are all GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. Ingredients and related target genes of Pulsatilliae Radix, Radix Salviae, Capsellae Herba, Hippophae Fructus, and Ginkgo Folium were obtained from TCMSP.[21] Both OB (oral bioavailability) value ≥ 40% and DL (drug similarity) value ≥ 0.18 are considered significant.[22]

Analysis of differentially expressed genes

GEO2R ( is an application for differential analysis of expression profiling chips based on the GEO database. We use GEO2R to detect the difference between glioma tissue and adjacent tissues and calculate the adjusted P value and | logFC |. Which meets the adjusted P < 0.01 and | logFC | ≥ 2 gene is considered significant which is the difference expressed between genes.[23] Each dataset was analyzed and the Venn diagram webtool ( was used to plot the intersecting parts of the three datasets. Then using Perl software (version; the effective components and related targets of five Chinese herbal medicines were combined. Finally, Perl software was used to find the intersection of differentially expressed genes and Chinese herbal medicine targets.

The network of active ingredients’ target genes and PPI network

The therapeutic target differentially expressed genes (DEGs) were screened utilizing Perl software in conjunction with the active ingredient, target gene, and DEGs. The target DEGs’ key components and their relationships were obtained. Cytoscape (version 3.8.2), an open software for molecular visualization, combined with DEGs was used to construct the active ingredient target network. The Bisogenet package of Cytoscape software was used to construct a PPI network for the target DEGs. The PPI network data was created from HPRD, BIND, DIP, MINT, INTACT, and BIOGRID. Input node and its neighbors up to a distance of 1 were necessary. The CytoNCA package of Cytoscape software was used for both PPI (protein-protein interaction) network topology analysis and for DC (degree) analysis, which satisfies the degree value > 120. Then, the key components with therapeutic significance and their corresponding key genes were obtained.

GO and KEGG analysis

GO analysis is a common method for large-scale functional enrichment studies. There are three ontologies of GO to describe the molecular functions (MF), cellular components (CC), and biological processes (BP) of genes. KEGG is a database that systematically analyzes gene function and links genomic and functional information, including metabolic pathway database, hierarchical classification database, gene database, genome database, etc. In this study, DAVID (the database for annotation, visualization and integrated discovery) ( was used to conduct GO annotation analysis and KEGG pathway enrichment analysis online. Among them, FDR < 0.01 and count ≥ 20 are considered statistically significant. Finally, we used bioinformatics ( to draw a bubble chart of the analysis results.

Hub gene acquisition and correlation analysis

In this research, we used STRING ( (version 11.5) online database to construct the PPI network of DEGs. In addition, the interaction between each combined hub greater than 0.4 is considered statistically significant. Cytoscape is a software that focuses on open network visualization and analysis. Its core is to provide a basic functional layout and query network and form a visual network based on the combination of basic data. We use the CytoHubba plug-in in Cytoscape to calculate the degree of each protein node. In this study, the first fifteen genes with close connections between nodes were identified as hub genes. Then the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) was used to construct the PPI network of hub genes. Second, the Gene Set Cancer Analysis ( constructed the interacting hub genes and the relationship between the hub genes and immune cell infiltration. Finally, in The Cancer Genome Atlas (TCGA) database (, the survival R package was used to do survival analysis (KM analysis) for hub gene mRNA through the log-rank method. We used P < 0.05 as the screening condition to obtain the survival curve.

Component–target molecular docking

A Three-dimensional (3D) structure of 3-wheel targets from the RCSB PDB database ( was downloaded. Protein was isolated using AutoDock Tools 1.5.6 software, water molecules were removed, Gasteiger charges were calculated, nonpolar hydrogen of the structure was added, and saved as a PDBQT file. PubChem database was used ( to download the top 2 two-dimensional structures. The two-dimensional structure was processed, the minimum free energy was calculated, converted into PDB format by Chem3D, and stored in AutoDock Tools 1.5.6 as a docking ligand in PDBQT format. The active site of molecular docking is determined by the coordination of ligands in the target protein complex. The ligand is flexible, while the receptor is rigid. Autodock Vina 1.1.2 docked small molecules with target proteins. In the docking results, a total of 20 conformations were generated in each group. The conformation with the best affinity was selected as the final docking conformation and visualized in Pymol 2.3.


Data acquisition

Three databases were selected for this study. Among them, the GSE35493 dataset contains 12 glioblastoma samples and 7 adjacent tissue samples, GSE50161 embodies 34 glioblastoma samples and 13 paracancerous tissue samples, and GSE104291 includes 4 samples of glioblastoma and 2 samples of adjacent tissues. In TCMSP, 7 active ingredients and 341 targets were extracted from Pulsatilliae Radix; 43 active ingredients and 2561 targets were obtained from Radix Salviae; 9 active ingredients and 1099 targets were acquired from Capsellae Herba; Extracted 20 active ingredients and 2448 targets from Hippophae Fructus; Eighteen active components and 2154 targets were gained from Ginkgo Folium.

Analysis of differentially expressed genes and construction of active ingredient target network

Among the differentially expressed genes that meet the adjusted P < 0.01 and | logFc | ≥ 2, there are 3570 genes in the GSE35493 dataset; 1479 up-regulated and 2091 down-regulated [Figure 1A], 2116 genes in the GSE50161 dataset; 876 up-regulated and 1240 down-regulated [Figure 1B], and there were 1729 genes in the GSE104291 dataset; 839 up-regulated, and 890 down-regulated [Figure 1C]. Venn diagram was used to draw the intersection of up-regulated and down-regulated genes. Among them, 332 common genes were upregulated [Figure 1D] and 579 common genes were downregulated [Figure 1E]. Based on the DEGs of the above three databases, a PPI network was constructed [Figure 2A]. The Bisogenet and cytoNCA packages of Cytoscape software were used for DC (degree) analysis of the obtained differentially expressed genes, and the key genes with therapeutic significance were obtained based on the degree value > 120 [Figure 2B]. The DEGs of GBM were combined with the active ingredient targets through Perl to obtain the active ingredient target network. Through Cytoscape, 22 target DEGs (CDK1, CCNB1, TOP2A, CAV1, CASP8, CCNA2, PLAU, LPL, COL3A1, SERPINE1, DTYMK, BIRC5, HMOX1, IL10RB, CDK2, PRKCB, CHEK1, RUNX1T1, MMP2, PLAT, CHRM3, EDNRA) and 31 key ingredients (MOL000098, MOL000354, MOL000422, MOL000449, MOL000492, MOL008488, MOL004564, MOL002879, MOL001697, MOL013306, MOL007119, MOL007041, MOL002651, MOL007111, MOL007108, MOL007105, MOL007098, MOL007094, MOL007154, MOL007088,  MOL007132, MOL007081, MOL007125, MOL007069, MOL007050, MOL001985, MOL001978, MOL001558, MOL011594, MOL005100, MOL000433) to construct an active ingredient target network were obtained.

Figure 1.
Figure 1. Volcano map of DEGs and venn diagram of DEGs common to all three GEO datasets. (A) volcanic maps for DEGs of GSE35493; (B) volcanic maps for DEGs of GSE50161; (C) volcanic maps for DEGs of GSE104291. Red dots: significantly upregulated genes, Blue dots: significantly downregulated genes, Black dots: nondifferentially expressed genes; (D) Up regulated genes; (E) Down regulated genes.

Figure 2.
Figure 2. PPI network for the target DEGs and topological analysis of genes after molecular docking. (A) Red indicates upregulated genes and green indicates downregulated genes; (B) The Bisogenet package and cytoNCA package of Cytoscape software were used for DC (degree) analysis, and the key genes with therapeutic significance were obtained based on a degree value > 120.

GO and KEGG analysis

GO and KEGG enrichment analysis of DEGs was performed using DAVID. GO analysis showed that DEGs were mainly concentrated in CC, including the plasma membrane, integral components of the plasma membrane, cell junctions, perinuclear regions of cytoplast, dendrites, cell surface, postsynaptic density, postsynaptic membrane, axon, neuron projections, synapse, microtubule, neural cell body, and extracellular matrix; BP analysis showed that DEGs were significantly enriched in signal transmission, chemical synaptic transmission, cell division, energetic system development, mitotic nuclear division, G2/M transition of the mitotic cell cycle, response to hypoxia, regulation of ion transmembrane transport and calcium ion transmembrane 

transport; For MF, DEGs are mainly enriched in protein binding, calcium ion binding and microtubule binding [Figure 3A]. KEGG analysis results show that DEGs are mainly enriched in neuroactive ligand-receptor interaction, oxytocin signaling pathway, calcium signaling pathway, retrograde endocannabinoid signaling, the cyclic adenosine monophosphate (cAMP) signaling pathway, GABAergic synapse, morphine addition, proteoglycans in cancer, adrenergic signaling in cardiology, and glutamatergic synapse [Figure 3B].

Figure 3.
Figure 3. GO and KEGG analysis in glioma. (A) GO analysis classified the DEGs into 3 groups (molecular function, biological process, and cellular components); (B) DEGs functional and signaling pathway enrichment were conducted using online websites of KEGG PATHWAY, Reactomen, BioCyc, Panther, NHGRI and Gene Ontology analysis.

Hub gene acquisition and correlation analysis

Using the CytoHubba plug-in in cytoscape, 15 hub genes are obtained through the degree of connection in the PPI network, which are CDK1, CCNA2, CCNB1, TOP2A, ASPM, CCNB2, BUB1, AURKA, NDC80, DLGAP5, KIF11, NCAPG, UBE2C, MAD2L1, KIF20A [Figure 4A]. Then, the PPI network of hub genes was obtained by STRING [Figure 4B]. Further, the correlation between hub genes in glioblastoma [Figure 4C] and the relationship between hub genes and immune cell infiltration [Figure 4D] were obtained from Gene Set Cancer Analysis (http://bio­ Based on the information in the TCGA database, the survival R package was used to obtain the survival curve [Figure 5A] and the box plot was used for the differential expression of the hub gene [Figure 5B] in gliomas. The results showed a significant positive correlation of hub genes with B cell, CD8+ naïve and gamma delta, and a significant negative correlation with CD4+ T cell, CD4+ naïve, and NKT. In glioblastoma, the vast majority of hub genes are significantly overexpressed and associated with a poor prognosis.

Figure 4.
Figure 4. PPI network of hub gene and its correlation in glioblastoma, and the relationship between hub genes and immune cell infiltration. (A) 15 hub genes; (B) PPI network of hub genes; (C) the correlation between hub genes in glioblastoma; (D) the relationship between hub genes and immune cell infiltration.

Figure 5.
Figure 5. Curve of hub gene and expression difference between normal and cancer samples. (A) The abscissa is the survival time and the ordinate is the survival rate. According to the median value of gene expression, the patients were divided into high and low groups. In the figure, red represents the high expression group and blue represents the low expression group. As can be seen from the figure, P < 0.05 indicates that there is a significant difference between high and low groups; (B) ‘Normal’ is the normal sample marked in blue, the data in brackets is the number of normal samples, the tumor is the cancer sample marked in red, and the data in brackets is the number of cancer samples. The ordinate represents the gene expression, p-value represents the significance level of the test, and p-value < 0.05 shows that there is a significant difference between the normal group and the cancer group.

Acquisition of effective components and potential therapeutic targets of TCM

By comparing the target network of active ingredients of TCM with hub genes and the therapeutically significant genes obtained through the Bisogenet package and CytoNCA package of Cytoscape software, we selected two hub genes for docking, namely CDK1 and CCNB1. Then, we obtained 5 protein-targeting active compounds from the compound-target gene interaction network, namely, MOL000098, MOL000354, MOL000422, MOL000449, and MOL000492. It can be seen from the Figure that MOL000098 is most closely related to the hub gene [Figure 6A], and its corresponding small molecule has its target in the hub gene [Figure 6B, 6C]. Because the monomer corresponding to MOL000098 in the pharmacology database and analysis platform of TCM system is quercetin. Therefore, it is speculated that quercetin is an effective TCM component potentially acting on CDK1 and CCNB1 in glioma.

Figure 6.
Figure 6. Compound-target gene interaction network and docking conformation of the small molecules with target protein. (A) Yellow is a common component of many traditional Chinese medicines, and the components represented by other colors correspond to one traditional Chinese medicine; (B) Docking conformation of quercetin and CDK1; (C) Docking conformation of quercetin and CCNB1.


Glioma is a malignant brain tumor with a complex microenvironment and a high degree of heterogeneity, and it has a poor prognosis.[24] The current treatment of glioma mainly includes surgery, radiotherapy, and chemotherapy. Even if the best therapy is used, the two-year survival rate of glioblastoma is still less than 30%. Although there is a relatively good prognosis in patients with low-grade glioma, treatment is almost never a cure.[25] TCM has long been used in the treatment of various diseases in China. In the study of active ingredients of drugs, the method based on network interaction helps to deepen the understanding of the role of drugs in multi-level information.[26] Therefore, the network of drugs, active ingredients, and disease interactions may have a key role in guiding the treatment of diseases. The target protein is an important way to express the mechanism of action of drugs and their active ingredients in the treatment of certain diseases, which helps to deepen the understanding of drug efficacy and provides a theoretical basis for the development of new drugs.[27] As an important pharmacological research method, online pharmacy and ecology can better prove the relationship between drugs, ingredients, and targets. Applying this idea to the study of TCM is helpful to understand the interaction mechanism of the multi-component and multitarget model TCM.[28]

In this study, gene expression and protein-protein expression analysis were performed based on public databases to identify potential key genes related to glioma. According to the gene expression profile data in the GEO database, the DEGs between the glioma tissue and adjacent normal tissues were screened out. In total, we identified 579 down-regulated DEGs and 332 up-regulated DEGs. These DEGs are mainly enriched in CCs, including the plasma membrane, integral components of the plasma membrane, cell junctions, perinuclear regions of cytoplasm, etc., and are significantly enriched in neuroactive ligand-receptor interaction, Oxytocin signaling pathway, Calcium signaling pathway, Retrograde endocannabinoid signaling, and cAMP signaling pathway in the KEGG signaling pathway. A PPI network was constructed using cystoscape to study the relationship between DEGs, and 15 hub genes were identified including CDK1, CCNA2, CCNB1, TOP2A, ASPM, CCNB2, BUB1, AURKA, NDC80, DLGAP5, KIF11, NCAPG, UBE2C, MAD2L1, KIF20A. All these genes are up-regulated in gliomas. Finally, based on the data in the TCGA database, the survival R package was used to predict the relationship between hub gene expression and the prognosis of glioma patients. It was found that the overexpression of all the above genes (except CCNB1 and MAD2L1) was associated with poor prognosis of glioma patients. The correlation between CCNB1 and prognosis of glioma was not clear in TCGA database, but CCNB1 could be screened as a potential therapeutic target for glioma through studies, and the specific function of CCNB1 remains to be explored.

Through network pharmacological analysis, our research results show that the 31 key components of five Chinese herbal medicines work by acting on 22 target DEGs. Among them, five key components are common ingredients of TCM and are potentially effective anticancer molecules. Two target genes (CDK1 and CCNB1) are hub genes with potential therapeutic targets for glioma.

Our results suggest that MOL000098 is more closely related to the target DEGs, and it is highly likely to become a key effective Chinese medicine monomer component in the treatment of glioma and has molecular drug therapy targets in both two pivotal genes. The corresponding monomer of MOL000098 is quercetin. Literature reports quercetin as an anti-inflammatory, antioxidant, antihistaminic, apoptosis and necrosis inducing, antiproliferative, and of cell cycle inhibitor in a variety of cancers including ovarian cancer, prostate cancer, esophageal cancer, breast cancer, hepatocellular carcinoma, and gastric cancer.[29],[30],[31],[32],[33],[34],[35],[36],[37] The literature reported that the study of quercetin and glioma is as follows. Quercetin inhibits the proliferation of glioma cells by inhibiting the expression of SREBPs and ChREBP and leading to the reduction of cholesterol and fatty acid synthesis in C6 glioma cells;[38] quercetin can disrupt intracellular calcium storage and strongly inhibit ATP-triggered responses in bend.3 cells.[39],[40] The main pathway enriched in DEGs in KEGG enrichment analysis is the calcium signaling pathway, suggesting that this signaling pathway is the mechanism of quercetin's action on glioma. Quercetin induces apoptosis of glioblastoma cells by inhibiting Axl/IL-6/STAT3 signaling pathway and AKT and ERK signaling pathway.[41],[42] However, there is no relevant literature research on the targeted therapy of quercetin on CDK1 and CCNB1. Quercetin, a TCM component selected in this study based on a public database, is very likely to become a key monomer component for the treatment of glioma, and the two potential therapeutic genes screened correspondingly may become its corresponding molecular drug therapy targets.


The results suggested that quercetin plays a potential therapeutic role in inhibiting glioma proliferation by targeting CDK1 and CCNB1. This study provides a new way to uncover quercetin for the treatment of gliomas.



This manuscript was presented as a poster in Brain Tumor Res Treat in abstract form at the WFNOS 2022 conference.



This article was partially supported by the grant from the Anhui Provincial Natural Science Foundation (No.2208085MH251) to Xingliang Dai.



The authors have no conflicts of interest to declare.



The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Institutional ethical approval and informed consent were waived.



(I) Conception and design: H Cheng and X Dai; (II) Administrative support: H Cheng and X Dai; (III) Provision of study materials or patients: H Li, P Gao and H Tian; (IV) Collection and assembly of data: H Li and Z Han; (V) Data analysis and interpretation: H Li and P Gao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.



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Raquel Luque Caro, Carmen Sánchez Toro, Lucia Ochoa Vallejo

Therapy‑Induced Histopathological Changes in Breast Cancers: The Changing Role of Pathology in Breast Cancer Diagnosis and Treatment

Shazima Sheereen1, Flora D. Lobo1, Waseemoddin Patel2, Shamama Sheereen3,
Abhishek Singh Nayyar4, Mubeen Khan5

Glioma Research in the Era of Medical Big Data

Feiyifan Wang1, Christopher J. Pirozzi2, Xuejun Li1

Transarterial Embolization for Hepatocellular Adenomas: Case Report and Literature Review

Jian‑Hong Zhong1,2, Kang Chen1, Bhavesh K. Ahir3, Qi Huang4, Ye Wu4, Cheng‑Cheng Liao1, Rong‑Rong Jia1, Bang‑De Xiang1,2, Le‑Qun Li1,2

Nicotinamide Phosphoribosyltransferase: Biology, Role in Cancer, and Novel Drug Target

Antonio Lucena‑Cacace1,2,3, Amancio Carnero1,2

Enhanced Anticancer Effect by Combination of Proteoglucan and Vitamin K3 on Bladder Cancer Cells

Michael Zhang, Kelvin Zheng, Muhammad Choudhury, John Phillips, Sensuke Konno

Molecular Insights Turning Game for Management of Ependymoma: A Review of Literature

Ajay Sasidharan, Rahul Krishnatry

IDH Gene Mutation in Glioma

Leping Liu1, Xuejun Li1,2

Challenges and Advances in the Management of Pediatric Intracranial Germ Cell Tumors: A Case Report and Literature Review

Gerard Cathal Millen1, Karen A. Manias1,2, Andrew C. Peet1,2, Jenny K. Adamski1

Assessing the Feasibility of Using Deformable Registration for Onboard Multimodality‑Based Target Localization in Radiation Therapy

Ge Ren1,2,3, Yawei Zhang1,2, Lei Ren1,2

Research Advancement in the Tumor Biomarker of Hepatocellular Carcinoma

Qing Du1, Xiaoying Ji2, Guangjing Yin3, Dengxian Wei3, Pengcheng Lin1, Yongchang Lu1,
Yugui Li3, Qiaohong Yang4, Shizhu Liu5, Jinliang Ku5, Wenbin Guan6, Yuanzhi Lu7

Novel Insights into the Role of Bacterial Gut Microbiota in Hepatocellular Carcinoma

Lei Zhang1, Guoyu Qiu2, Xiaohui Xu2, Yufeng Zhou3, Ruiming Chang4

Central Odontogenic Fibroma with Unusual Presenting Symptoms

Aanchal Tandon, Bharadwaj Bordoloi, Safia Siddiqui, Rohit Jaiswal

The Prognostic Role of Lactate in Patients Who Achieved Return of Spontaneous Circulation after Cardiac Arrest: A Systematic Review and Meta‑analysis

Dongni Ren1, Xin Wang2, Yanyang Tu1,2

Inhibitory Effect of Hyaluronidase‑4 in a Rat Spinal Cord Hemisection Model

Xipeng Wang1,2, Mitsuteru Yokoyama2, Ping Liu3

Research and Development of Anticancer Agents under the Guidance of Biomarkers

Xiaohui Xu1, Guoyu Qiu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Ruling Jia1, Bo Zhao1

Idiopathic Hypereosinophilic Syndrome and Disseminated Intravascular Coagulation

Mansoor C. Abdulla

Phosphorylation of BRCA1‑Associated Protein 1 as an Important Mechanism in the Evasion of Tumorigenesis: A Perspective

Guru Prasad Sharma1, Anjali Geethadevi2, Jyotsna Mishra3, G. Anupa4, Kapilesh Jadhav5,
K. S. Vikramdeo6, Deepak Parashar2

Progress in Diagnosis and Treatment of Mixed Adenoneuroendocrine Carcinoma of Biliary‑Pancreatic System

Ge Zengzheng1, Huang-Sheng Ling2, Ming-Feng Li2, Xu Xiaoyan1, Yao Kai1, Xu Tongzhen3,
Ge Zengyu4, Li Zhou5

Surface-Enhanced Raman Spectroscopy to Study the Biological Activity of Anticancer Agent

Guoyu Qiu1, Xiaohui Xu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Huan Yang5

Alzheimer’s Disease Susceptibility Genes in Malignant Breast Tumors

Steven Lehrer1, Peter H. Rheinstein2

OSMCC: An Online Survival Analysis Tool for Merkel Cell Carcinoma

Umair Ali Khan Saddozai1, Qiang Wang1, Xiaoxiao Sun1, Yifang Dang1, JiaJia Lv1,2, Junfang Xin1, Wan Zhu3, Yongqiang Li1, Xinying Ji1, Xiangqian Guo1

Protective Activity of Selenium against 5‑Fluorouracil‑Induced Nephrotoxicity in Rats

Elias Adikwu, Nelson Clemente Ebinyo, Beauty Tokoni Amgbare

Advances on the Components of Fibrinolytic System in Malignant Tumors

Zengzheng Ge1, Xiaoyan Xu1, Zengyu Ge2, Shaopeng Zhou3, Xiulin Li1, Kai Yao1, Lan Deng4

A Patient with Persistent Foot Swelling after Ankle Sprain: B‑Cell Lymphoblastic Lymphoma Mimicking Soft‑tissue Sarcoma

Crystal R. Montgomery‑Goecker1, Andrew A. Martin2, Charles F. Timmons3, Dinesh Rakheja3, Veena Rajaram3, Hung S. Luu3

Coenzyme Q10 and Resveratrol Abrogate Paclitaxel‑Induced Hepatotoxicity in Rats

Elias Adikwu, Nelson Clemente Ebinyo, Loritta Wasini Harris

Progress in Clinical Follow‑up Study of Dendritic Cells Combined with Cytokine‑Induced Killer for Stomach Cancer

Ling Wang1,2, Run Wan1,2, Cong Chen1,2, Ruiliang Su1,2, Yumin Li1,2

Supraclavicular Lymphadenopathy as the Initial Manifestation in Carcinoma of Cervix

Priyanka Priyaarshini1, Tapan Kumar Sahoo2

ABO Typing Error Resolution and Transfusion Support in a Case of an Acute Leukemia Patient Showing Loss of Antigen Expression

Debasish Mishra1, Gopal Krushna Ray1, Smita Mahapatra2, Pankaj Parida2

Protein Disulfide Isomerase A3: A Potential Regulatory Factor of Colon Epithelial Cells

Yang Li1, Zhenfan Huang2, Haiping Jiang3

Clinicopathological Association of p16 and its Impact on Outcome of Chemoradiation in Head‑and‑Neck Squamous Cell Cancer Patients in North‑East India

Srigopal Mohanty1, Yumkhaibam Sobita Devi2, Nithin Raj Daniel3, Dulasi Raman Ponna4,
Ph. Madhubala Devi5, Laishram Jaichand Singh2

Potential Inhibitor for 2019‑Novel Coronaviruses in Drug Development

Xiaohui Xu1, Zilong Dang2, Lei Zhang3, Lingxue Zhuang4, Wutang Jing5, Lupeng Ji6, Guoyu Qiu1

Best‑Match Blood Transfusion in Pediatric Patients with Mixed Autoantibodies

Debasish Mishra1, Dibyajyoti Sahoo1, Smita Mahapatra2, Ashutosh Panigrahi3

Characteristics and Outcome of Patients with Pheochromocytoma

Nadeema Rafiq1, Tauseef Nabi2, Sajad Ahmad Dar3, Shahnawaz Rasool4

Comparison of Histopathological Grading and Staging of Breast Cancer with p53‑Positive and Transforming Growth Factor‑Beta Receptor 2‑Negative Immunohistochemical Marker Expression Cases

Palash Kumar Mandal1, Anindya Adhikari2, Subir Biswas3, Amita Giri4, Arnab Gupta5,
Arindam Bhattacharya6

Chemical Compositions and Antiproliferative Effect of Essential Oil of Asafoetida on MCF7 Human Breast Cancer Cell Line and Female Wistar Rats

Seyyed Majid Bagheri1,2, Davood Javidmehr3, Mohammad Ghaffari1, Ehsan Ghoderti‑Shatori4

Cyclooxygenase‑2 Contributes to Mutant Epidermal Growth Factor Receptor Lung Tumorigenesis by Promoting an Immunosuppressive Environment

Mun Kyoung Kim1, Aidin Iravani2, Matthew K. Topham2,3

Potential role of CircMET as A Novel Diagnostic Biomarker of Papillary Thyroid Cancer

Yan Liu1,2,3,4#, Chen Cui1,2,3,4#, Jidong Liu1,2,3,4, Peng Lin1,2,3,4,Kai Liang1,2,3,4, Peng Su5, Xinguo Hou1,2,3,4, Chuan Wang1,2,3,4, Jinbo Liu1,2,3,4, Bo Chen6, Hong Lai1,2,3,4, Yujing Sun1,2,3,4* and Li Chen 1,2,3,4*

Cuproptosis-related Genes in Glioblastoma as Potential Therapeutic Targets

Zhiyu Xia1,2, Haotian Tian1, Lei Shu1,2, Guozhang Tang3, Zhenyu Han4, Yangchun Hu1*, Xingliang Dai1*

Cancer Diagnosis and Treatments by Porous Inorganic Nanocarriers

Jianfeng Xu1,2, Hanwen Zhang1,2, Xiaohui Song1,2, Yangong Zheng3, Qingning Li1,2,4*

Delayed (20 Years) post-surgical Esophageal Metastasis of Breast Cancer - A Case Report

Bowen Hu1#, Lingyu Du2#, Hongya Xie1, Jun Ma1, Yong Yang1*, Jie Tan2*

Subtyping of Undifferentiated Pleomorphic Sarcoma and Its Clinical Meaning

Umair Ali Khan Saddozai, Zhendong Lu, Fengling Wang, Muhammad Usman Akbar, Saadullah Khattak, Muhammad Badar, Nazeer Hussain Khan, Longxiang Xie, Yongqiang Li, Xinying Ji, Xiangqian Guo

Construction of Glioma Prognosis Model and Exploration of Related Regulatory Mechanism of Model Gene

Suxia Hu, Abdusemer Reyimu, Wubi Zhou, Xiang Wang, Ying Zheng, Xia Chen, Weiqiang Li, Jingjing Dai

ESRP2 as a Non-independent Potential Biomarker-Current Progress in Tumors

Yuting Chen, Yuzhen Rao, Zhiyu Zeng, Jiajie Luo, Chengkuan Zhao, Shuyao Zhang

Resection of Bladder Tumors at the Ureteral Orifice Using a Hook Plasma Electrode: A Case Report

Jun Li, Ziyong Wang, Qilin Wang

Structural Characterization and Bioactivity for Lycium Barbarum Polysaccharides

Jinghua Qi1,2,  Hangping Chen3,Huaqing Lin2,4,Hongyuan Chen1,2,5* and Wen Rui2,3,5,6*

The Role of IL-22 in the Prevention of Inflammatory Bowel Disease and Liver Injury

Xingli Qi1,2, Huaqing Lin2,3, Wen Rui2,3,4,5 and Hongyuan Chen1,2,3

RBM15 and YTHDF3 as Positive Prognostic Predictors in ESCC: A Bioinformatic Analysis Based on The Cancer Genome Atlas (TCGA)

Yulou Luo1, Lan Chen2, Ximing Qu3, Na Yi3, Jihua Ran4, Yan Chen3,5*

Mining and Analysis of Adverse Drug Reaction Signals Induced by Anaplastic Lymphoma Kinase-Tyrosine Kinase Inhibitors Based on the FAERS Database

Xiumin Zhang1,2#, Xinyue Lin1,3#, Siman Su1,3#, Wei He3, Yuying Huang4, Chengkuan Zhao3, Xiaoshan Chen3, Jialin Zhong3, Chong Liu3, Wang Chen3, Chengcheng Xu3, Ping Yang5, Man Zhang5, Yanli Lei5*, Shuyao Zhang1,3*

Advancements in Immunotherapy for Advanced Gastric Cancer

Min Jiang1#, Rui Zheng1#, Ling Shao1, Ning Yao2, Zhengmao Lu1*

Tumor Regression after COVID-19 Infection in Metastatic Adrenocortical Carcinoma Treated with Immune Checkpoint Blockade: A Case Report

Qiaoxin Lin1, Bin Liang1, Yangyang Li2, Ling Tian3*, Dianna Gu1*

Mining and Analysis of Adverse Events of BRAF Inhibitors Based on FDA Reporting System

Silan Peng1,2#, Danling Zheng1,3#, Yanli Lei4#, Wang Chen3, Chengkuan Zhao3, Xinyue Lin1, Xiaoshan Chen3, Wei He3, Li Li3, Qiuzhen Zhang5*, Shuyao Zhang1,3*

Malignant Phyllodes Tumor with Fever, Anemia, Hypoproteinemia: A Rare and Strange Case Report and Literature Review

Zhenghang Li1, Yuxian Wei1*

Construction of Cuproptosis-Related LncRNA Signature as a Prognostic Model Associated with Immune Microenvironment for Clear-Cell Renal Cell Carcinoma

Jiyao Yu1#, Shukai Zhang2#, Qingwen Ran3, Xuemei Li4,5,6*

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