Cancer Translational Medicine

Original Research | Open Access

Vol.9 (2023) | Issue-2 | Page No: 65-76

DOI: https://doi-ds.org/doilink/06.2023-24133173/A2

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

Affiliations  

1. Department of Medical Laboratory, Huainan First People's Hospital, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan, Anhui, China

2 Medical College, Anhui University of Science and Technology, Huainan, Anhui, China

3. Department of Pathology, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China

4. Department of Paediatrics, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China

5. Department of Anesthesiology, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China

6. Department of Medical Laboratory, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China


Important Dates  

Date of Submission:   24-May-2023

Date of Acceptance:   21-Jun-2023

Date of Publication:   30-Jun-2023

ABSTRACT

Objective: To analyze the prognostic role of differentially expressed genes (DEGs) in glioma.

Methods: Kaplan-Meier (KM) and univariate Cox analysis were used to screen the common prognostic genes of LGG and GBM. Multivariate Cox regression analysis was included for further analysis. Prognostic risk models were constructed and the risk score of each patient was calculated.  The HPA database was used to analyze the expression of model genes. GSCAlite platform was used to analyze model genes' regulatory network and drug sensitivity. TIMER analyzed the correlation between model genes and immune infiltration.

Results: Multivariate Cox regression analysis showed that patient age, tumor grade, and patient risk score were independent risk factors for LGG prognosis. PTPRN and RGS14 were under-expressed in gliomas, and there was a synergistic effect on TSC/mTOR but inhibited RAS/MAPK, hormone AR and ER pathways in LGG. Over-expressed MTHFD2 and HOXB2 showed antagonistic effects with PTPRN and RGS14. Afatinib, gefitinib, trametinib, methotrexate, FK866 and vorinostat were more sensitive to model genes. The expression of FERMT1, HOXB2, and PTPRN was significantly correlated with the immune infiltration level of LGG.

Conclusions: The prognostic risk model, molecular mechanism, and regulation of model genes play an important role in glioma.

Keywords: Low-grade gliomas (LGG), glioblastoma (GBM), prognosis, risk score, regulation, survival


INTRODUCTION

Neuroepithelial tumors are collectively referred to as gliomas, accounting for 40% - 50% of brain tumors.[1] They are the most common primary intracranial tumors. According to the classification criteria of glioma by the World Health Organization (WHO), pathology and the latest molecular biological characteristics, glioma can be divided into grades I - IV. Grade I and II are low-grade gliomas (LGG), which account for about 20% of all primary brain tumors.[2] They are well-differentiated gliomas. Although the malignant potential of LGG is low and the prognosis of patients is good after surgical treatment, LGG is still invasive and can progress to glioblastoma.[3] Grade III and IV are high-grade gliomas (HGG), which are poorly differentiated malignant tumors with poor prognosis. Grade IV glioblastoma (GBM) is the most invasive of all malignant gliomas, accounting for about 55% of malignant gliomas.[4] GBM cells are highly invasive and diffuse, so it is difficult to completely remove them. In addition, GBM is highly resistant to radiotherapy and chemotherapy. Therefore, accurate molecular targeted therapy is an important direction of future therapy.[5] The pathogenesis of glioma involves many factors and steps, including genetic and epigenetic changes.[6] Gene abnormalities are accompanied by changes in expression patterns, which is of great significance in the early diagnosis, treatment monitoring, and prognosis evaluation of glioma.

At present, some gene expressions are used as markers for the diagnosis and prognosis of glioma. For example, isocitrate dehydrogenase (IDH) mutations are important markers of low-grade glioma (LGG) and glioblastoma (GBM).[7] It has significant prognostic value: Patients with IDH gene mutation have a better prognosis, and have a longer progression-free survival and overall survival. The prognosis of IDH wild-type patients is poor, and the progression-free survival and overall survival are relatively short. IDH mutation status can assist in the diagnosis of glioma.[8] Phosphatase and Tensin Homolog (PTEN) is an important tumor suppressor gene and an evaluation index of tumor prognosis. About 86% of GBM patients will have changes in RTK/PI3K pathway caused by PTEN gene deletion or mutation.[9] However, we still know little about the exact mechanism of the occurrence and development of glioma, so the important molecular markers related to the occurrence and development of glioma still need to be further explored, so as to improve the accuracy of early diagnosis of glioma and the prediction efficiency of prognosis.

With the continuous maturity of high-throughput technologies such as gene chips and RNA sequencing, much data related to gene expression can be presented.[10] Computer technology combined with bioinformatics has been widely used in tumor research. Its effectiveness and reliability in finding new markers for tumor diagnosis and targeted therapy have been proven.[11] However, it has been a difficult problem in clinical applications to analyze the prediction efficiency of key genes to improve the accuracy of tumor prognostic effects. Therefore, based on The Cancer Genome Atlas (TCGA), five common differentially expressed genes (DEGs) related to prognosis were screened in common glioma types (LGG and GBM). The Cox regression prognostic risk model was constructed. The receiver operating characteristic (ROC) curve and Kaplan-Meier (KM) curve were used to evaluate the prediction accuracy and reliability of the multivariate Cox risk model. Subsequently, regulatory pathways, drug sensitivity, and the correlation with immune infiltration involved in the five prognostic genes were explored, so as to provide an important theoretical basis for explaining their pathogenesis and therapeutic targets.


MATERIALS AND METHODS

Data collection

The transcription sequencing data of 529 LGG patients, 169 GBM patients, and 5 normal control groups were obtained from the TCGA database (https://portal.gdc.cancer.gov/), and the clinical data of patients were downloaded, including overall survival time, survival status, age, gender, and tumor grade. Downloaded the human gene annotation file from the Ensembl database (http://asia.ensembl.org/index.html) and converted the probe to the corresponding gene name.

Extraction and screening of DEGs in tumor and normal brain tissues

The expression intensity of gene expression profile data of two tumors (LGG and GBM) and normal brain tissues were normalized by R language. The DEGs between tumor tissue and normal brain tissue were screened by the R language “limma” package. Screening conditions were set as difference multiple > 4 times and P < 0.05 (log fold change> 2 & Adjusted P < 0.05).

Identification of prognostic genes

The standardized expression of DEGs was combined with the survival information of tumor samples. After excluding patients with no survival time records, a total of 508 LGG samples and 158 GBM samples were included in the survival analysis. KM and univariate survival analysis was performed on the expression of DEGs in two kinds of glioma (LGG and GBM) by using the "survival" package of the R language. The gene expression with a P value less than 0.01 was selected for further construction of the model. Five overlapping prognostic genes in LGG and GBM were screened by Venn map for constructing a prognostic model.

Establishment and validation of the prognostic model

Multivariate Cox analysis was performed on the five selected prognostic genes. The prediction model was established with survival status and survival time as dependent variables and gene expression as independent variables. The risk regression model was established by using the expression of five genes and their coefficients. Subsequently, the risk score of all patients was calculated using this prognostic model. considering the median risk score as the dividing point, the patients with a risk score greater than the median risk score is the high-risk group, and the remaining belong to the low-risk group. KM analysis was used to compare the difference in the survival rate between the high-risk and the low-risk group. The feasibility and accuracy of the model are verified by the ROC curve. Then, univariate and multivariate Cox regression analyses were carried out that were correlated with clinical factors to comprehensively evaluate the prediction efficiency of the risk scoring system.

Expression, regulatory network, and drug sensitivity of prognostic genes

The expression of five prognostic genes in gliomas and normal cerebellar tissues was mined from the human protein atlas database (https://www.proteinatlas.org). GSCALite is a gene set cancer analysis platform that includes and integrates cancer genomics data of 33 cancer types from TCGA, drug response data from GDSC and CTRP, and normal tissue data from GTEX.[12] We explored the possible regulatory mechanisms and regulatory networks of five prognostic genes in tumors from the GSCALite platform. Finally, drug targets for five prognostic genes were analyzed, and the structure was visualized by the DrugBank database.

Correlation between prognostic genes and immune infiltration

The TIMER database evaluates the infiltration of different immune cells and their clinical impact according to its unique algorithm and module system.[13] Five prognostic genes screened were input through the "gene module" to generate a scatter diagram to observe the relationship between their expression and the level of immune infiltration in gliomas (LGG and GBM).


RESULTS

Construction of glioma risk model based on prognosis-related DEGs

The mRNA expression matrix was extracted from the LGG and GBM gene transcription data downloaded from the TCGA database. Compared with normal tissues, 1,336 DEGs were screened in LGG tissues [Figure 1A] and 3,184 DEGs were screened in GBM tissues [Figure 1B]. Subsequently, we performed KM and univariate Cox survival analysis on DEGs. KM analysis showed that there were 444 DEGs and 50 DEGs related to the prognosis of LGG and GBM, respectively. Univariate Cox analysis showed that there were 559 DEGs and 110 DEGs related to the prognosis of LGG and GBM, respectively. Among them, five DEGs are common prognostic genes of LGG and GBM [Figure 1C], namely MTHFD2, FERMT1, PTPRN, HOXB2, and RGS14. Multivariate Cox regression analysis was performed on LGG and GBM based on five prognostic genes [Figure 1D, 1E].

Figure 1.
Figure 1. Screening of prognostic related DEGs in gliomas and construction of risk model. (A) Differentially expressed mRNA screened from LGG. (B) Differentially expressed mRNA screened from GBM. Red dots represent up-regulated genes and green dots represent down-regulated genes. (C) KM and univariate Cox methods were used to analyze the differentially expressed genes of LGG and GBM respectively, and the overlapping genes were displayed by Venn diagram. (D) Multivariate analysis of five prognostic genes in LGG. (E) Multivariate analysis of five prognostic genes in GBM.

Evaluation of prognostic risk model

Prognostic risk models of LGG and GBM were constructed based on five prognostic genes, and the risk score of each patient was calculated. Risk score=   gene expression i×Model coefficient i. The patients were divided into a low-risk group and a high-risk group based on the median risk value [Figure 2A, 2F]. It was found that in LGG, MTHFD2, FERMT1, and PTPRN genes decreased with the increase in the risk score, while HOXB2 and RGS14 genes were opposite [Figure 2B]. In GBM, PTPRN, HOXB2, and RGS14 genes increased with the increase in the risk score, while MTHFD2 and FERMT1 genes were opposite [Figure 2G]. In both LGG and GBM, patient survival decreased with the increase in the risk score [Figure 2C, 2H]. Then, KM survival analysis was performed and survival curves were drawn. The results showed that the survival rate of patients in the high-risk group of LGG and GBM was significantly lower than that in the low-risk group [Figure 2D, 2I]. At the same time, the prognostic risk model of LGG and GBM’s overall survival ROC curve was drawn. The AUC values of 1 year, 3 years, and 5 years in LGG were 0.844, 0.797, and 0.687, respectively [Figure 2E]. The AUC values of 1, 3, and 5 years of GBM are 0.75, 0.773, and 0.923, respectively [Figure 2J]. It shows that the prognostic risk model of LGG and GBM has good efficiency.

Figure 2.
Figure 2. Prognostic gene risk score model and its predictive effect evaluation. (A-E) The risk scoring model and performance evaluation results of five prognostic genes in LGG. (F-J) The risk scoring model and performance evaluation results of five prognostic genes in GBM.

Evaluation of risk model as an independent predictor

In order to evaluate whether the model can be independent of the clinical characteristics of patients, that is, it is not affected by the clinical characteristics, we performed univariate Cox analysis on the five gene individuals and comprehensive risk score, age, gender, and glioma grade in LGG and GBM. Univariate analysis showed that, except for gender, five gene individuals and comprehensive risk score and other clinical features were associated with the prognosis of LGG and GBM (P < 0.05) [Figure 3A, 3B]. Then, in order to explore whether there is an interaction between risk score and various clinical features and its impact on prognosis, we made a multivariate Cox regression analysis on five gene individuals, comprehensive risk score, and clinical features. In LGG, age, gender, glioma grade, five gene individuals, and comprehensive risk score were all associated with prognosis [Figure 3C-3H]. In GBM, an individual and comprehensive risk score of five genes was associated with prognosis [Figure 3I-3N]. The above results prove and emphasize the effectiveness and importance of the model constructed in this experiment.

Figure 3.
Figure 3. Five gene models were evaluated as independent predictors. Prognostic analysis of individual and comprehensive risk scores involving patient characteristics. (A-B) Univariate Cox analysis of prognostic factors of LGG and GBM. (C-H) Multivariate analysis of prognostic factors in LGG. (I-N) Multivariate analysis of prognostic factors in GBM.

Prognostic gene expression, regulatory network, and drug sensitivity analysis

The validation of prognostic genes in the human protein atlas database showed that MTHFD2 and FERMT1 were up-regulated in gliomas, while PTPRN and RGS14 were up-regulated in normal cerebellar tissues. However, the immunohistochemical test shows that there was no significant difference in the expression of PTPRN between glioma and normal cerebellar tissues, and HOXB2 was not detected in the database [Figure 4]. The possible regulatory mechanisms of five prognostic genes in 32 cancer types were explored on the GSCAlite platform. The results showed that MTHFD2 promoted apoptosis and cell cycle in most tumors. PTPRN promotes the EMT pathway and inhibits DNA damage response, hormone AR and hormone ER pathway in most tumors. HOXB2 promotes the EMT pathway in some tumors [Figure 5A]. However, in LGG, MTHFD2 can promote apoptosis, while PTPRN and RGS14 can inhibit this pathway. PTPRN and RGS14 can promote TSC/mTOR pathway, but inhibit Ras/MAPK, hormone AR and hormone ER pathway. MTHFD2 and HOXB2 can inhibit TSC/mTOR pathway and promote Ras/MAPK, hormone AR and hormone ER pathway [Figure 5B]. The results showed that MTHFD2 and HOXB2 may have antagonistic effects with PTPRN and RGS14. In GBM, there was no regulatory relationship between MTHFD2, FERMT1, PTPRN, HOXB2, RGS14 and pathway activity. In the regulatory analysis of miRNA prognostic genes, we predicted that 31 miRNAs had regulatory relationships with 5 prognostic genes [Figure 6]. Both hsa-mir-513a-3p and hsa-mir-660-5p can regulate MTHFD2 and FERMT1 genes. Hsa-mir-186-5p can regulate FERMT1 and HOXB2 at the same time. The results of drug sensitivity showed that afatinib, gefitinib, and trametinib had a good inhibitory effect on FERMT1. Methotrexate inhibited MTHFD2 and RGS14, while high expression of HOXB2 was resistant to the drug. FK866 and vorinostat also had a good inhibitory effect on MTHFD2 [Figure 7A]. Finally, the drug structure was visualized [Figure 7B].

Figure 4
Figure 4. Immunohistochemistry confirmed the expression of five prognostic genes in glioma and normal tissues. The brown area indicates positive expression.

Figure 5.
Figure 5. Pathway prediction analysis of five prognostic genes. (A) Global percentage of cancers in which genes affect pathways among 32 cancer types (number of cancer types activated or inhibited / 32 * 100%). (B) Possible pathways of five prognostic genes in LGG.

Figure 6
Figure 6. Potential miRNA regulatory network of prognostic genes. Yellow nodes represent genes and blue nodes represent miRNAs. The margin represents the regulation of genes by miRNA. The node size is positively correlated with the networking degree of the node, and the edge width is defined by the absolute value of the correlation coefficient.

Figure 7.
Figure 7. Drug resistance analysis of prognostic genes. In GDSC drug data, Spearman coefficient represents the correlation between gene expression and drugs. (A) Sensitive drug analysis of 5 prognostic genes. (B) Structural view of gene sensitive drugs.

Relationship between prognostic genes and immune infiltration in gliomas

The expression of FERMT1 was negatively correlated with the level of immune infiltration in LGG, but not significantly correlated with the level of immune infiltration in GBM. The expression of HOXB2 was positively correlated with the level of immune infiltration in LGG, but not significantly correlated with the level of immune infiltration in GBM. The expression of PTPRN was significantly correlated with the level of immune infiltration in glioma, which was negatively correlated with the level of immune infiltration in LGG, but positively correlated with the level of immune infiltration in GBM [Figure 8].

Figure 8
Figure 8. Systematic association analysis between five prognostic genes and immune infiltration level. Partial.cor represents the correlation between gene expression and immune infiltration level. P < 0.05, with statistical significance.


DISCUSSION

The most prevalent intracranial malignant tumor is glioma, which is often treated surgically. However, chemotherapy is frequently prescribed to treat nearly all newly diagnosed diffuse gliomas.[14] LGG and GBM are common types of gliomas in clinics, and the incidence rate is increasing year by year. A family history of brain tumors and the history of ionizing radiation exposure are clear risk factors for brain tumors, which can lead to genetic variation related to brain tumor susceptibility.[15] However, heterogeneity between cells in tumors leads to differences in gene expression, accompanied by differences in pathways related to cell function. In addition, synergistic or compensatory effect between tumor mechanisms caused by cancer-promoting factors also greatly limits the early diagnosis and treatment of tumors in clinical.[16] At present, the effect of markers used to diagnose and evaluate the prognosis of patients is not ideal. Detection indexes such as IDH-1, ATRX and P53 used in clinics also need a comprehensive evaluation to provide a reference for glioma diagnosis, but these indexes have poor prediction effects on prognosis.

In recent years, with the development of high-throughput bioinformatics technologies such as gene chips, gene microarray, and whole genome sequencing, the key genes in the occurrence and progression of diseases can be accurately excavated, which brings a new dawn for exploring the molecular pathogenesis of diseases, improving clinical diagnosis and gene targeted therapy.[17] In this study, DEGs were screened by analyzing LGG and GBM genomic data. Five common prognostic genes (MTHFD2, FERMT1, PTPRN, HOXB2, and RGS14) of LGG and GBM were screened by KM and univariate Cox regression analysis. They were included in multivariate Cox regression analysis, and prognostic risk models of LGG and GBM were constructed.

Methyl benzoate dehydrogenase (MTHFD2) belongs to the tetrahydrofolate dehydrogenase/ cyclohydrolase family, which mainly exists in mitochondria and participates in a single carbon metabolism pathway. Some studies have found that the expression of MTHFD2 is up-regulated in gliomas, and the high expression of MTHFD2 is conducive to the prognosis of LGG patients.[18] Consistent with this study, MTHFD2 is up-regulated in both LGG and GBM and can activate autophagy pathway in LGG. The good prognosis of patients with high MTHFD2 expression may be related to the over-activation of the autophagy pathway. MTHFD2 is a low-risk gene in the risk model, which can be used as a marker to predict the prognosis of patients. RGS14 promotes a rise in G-protein α, and the GTPase activity of the subunit inhibits signal transduction so that it enters the inactive GDP binding form. Similar studies showed that the expression of RGS14 was downregulated in GBM, and the prognosis of patients with high expression was poor. The same is true in LGG, and with the increase of RGS14 expression, the patient risk increases.[19] Fermitin family member 1 (FERMT1) belongs to the Kindlin family, which is overexpressed in colon cancer and activated by β-Catenin promotes epithelial-mesenchymal transition (EMT) and is associated with a low survival rate.[20] As in this study, FERMT1 was highly expressed in LGG and GBM. However, FERMT1 is a protective factor in the LGG and GBM, which can inhibit EMT and cell cycle pathways. Patients with high expression of FERMT1 have a better prognosis. FERMT1 may play an inhibitory role in glioma. Protein tyrosine phosphatase, receptor type N (PTPRN) plays a role in vesicle-mediated secretion. Some studies have found that the expression of PTPRN is downregulated in GBM, and the patients with high expression of PTPRN have poor prognosis,[21] which is consistent with our results. However, although PTPRN is also low expressed in LGG, patients with high expression have a better prognosis and are low-risk genes. The receptor tyrosine kinase (RTK) pathway plays a key role in LGG diffusion.[22] Inhibition of PI3K/Akt/mTOR makes LGG patients live longer,[23] PTPRN can inhibit the above pathway. In addition, the down-regulation of PTPRN expression in LGG increased the level of immune infiltration, while the level of immune infiltration in GBM decreased. Therefore, due to the different regulatory mechanisms of PTPRN on pathways and immune infiltration in LGG and GBM, its role in different tumors may be different. HOXB2 is a part of the developmental regulatory system, which provides cells with specific positional identities on the anterior and posterior axes. It belongs to the ANTP homeobox family. HOXB2 is related to cell invasion and promotes the proliferation of glioma cells.[24] In lung cancer and pancreatic cancer, upregulation of HOXB2 promotes tumor migration.[25] Activation of the RACK1/CNTN2/RTK/RAS/MAPK axis can promote the proliferation and inhibit the differentiation of glioma cells.[26] In this study, HOXB2 is highly expressed in LGG and GBM, which is a high-risk factor related to poor prognosis. It is also related to the activation of Ras/MAPK in LGG and can be used as an independent prognostic marker.

Although prognostic genes perform well in predicting prognosis alone, their comprehensive predictive ability is better. Therefore, we screened five common prognostic genes of LGG and GBM through survival analysis, incorporated them into multivariate Cox regression analysis, and constructed the prognostic risk models of LGG and GBM respectively. The patients were divided into a low-risk group and a high-risk group. The survival rate of patients in the high-risk group was significantly lower than that in the low-risk group, and the difference was statistically significant. AUC at 1, 3, and 5 years proved that the prognostic risk models of LGG and GBM were effective. At the same time, combined with the patient's clinicopathological information and risk value, a multivariate Cox regression analysis was carried out. It was found that the patient's age, tumor grade, and patient risk score were independent risk factors of LGG, while the patient risk score was the independent risk factor of GBM. This study proved that the risk score has an independent prognostic value and can be used as an independent prognostic predictor of glioma patients.

The change in the gene expression pattern of the tumor is accompanied by the change in drug effect and the degree of clinical drug resistance.[27] Our study found that the high expression of FERMT1 made it more sensitive to afatinib, gefitinib, and trametinib. Afatinib,[28] gefitinib,[29] and trametinib[30] have been reported to inhibit glioma. It is speculated that the high expression of FERMT1 may play a role in drug sensitivity. Methotrexate has anticancer activity in glioma.[31] Although HOXB2 is highly expressed and resistant to methotrexate, MTHFD2 and RGS14 are sensitive to this drug, and the mechanism of action needs to be further explored. FK866 also showed antitumor activity against glioma[32] and was most sensitive to the expression of MTHFD2. Sensitive drugs of key genes to provide more choices for the clinical treatment of gliomas needs to be further explored.


CONCLUSIONS

In conclusion, this study analyzed DEGs in LGG and GBM based on TCGA database, screened 5 genes, established prognostic risk model, and verified its good specificity and sensitivity. Subsequently, it was proved that the prognostic risk models of LGG and GBM were independent prognostic factors and could be used as biomarkers for prognostic evaluation. The five genes in the prognostic risk model also provide new therapeutic targets for exploring glioma. However, this study is only verified based on the database and lacks the support of clinical big data. The specific mechanism in tumor is still unclear and needs further research.

 

ACKNOWLEDGEMENT

We would like to thank the team members for their contributions to this paper, and then we will continue to work hard to do relevant research.

 

FINANCIAL SUPPORT AND SPONSORSHIP

This work was supported by the grants from Plan Project of Huainan Science and Technology in 2020 (No.2020069, to Suxia Hu) and General Project of Development Fund for Affiliated Hospital of Xuzhou Medical University in 2022 (No.XYFM202234, to Xiang Wang).

 

CONFLICTS OF INTEREST

The authors declare that they have no competing interests.

 

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

All research experiments involving patient data were approved by the Ethics Committee (approval number: KY-2022-014-01).

 

AUTHOR CONTRIBUTIONS

Jingjing Dai, Suxia Hu, Wubi Zhou and Xia Chen participated in the study design. Abdusemer Reyimu, Ying Zheng, Xiang Wang, Weiqiang Li, Wubi Zhou and Xia Chen statistically analyzed the data. The manuscript was drafted by Suxia Hu, Weiqiang Li and Xia Chen, and revised by Jingjing Dai and Weiqiang Li. The authors read and approved the final manuscript.


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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


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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