Cancer Translational Medicine

Original Research | Open Access

Vol.8 (2022) | Issue-4 | Page No: 116-150

DOI: https://doi-ds.org/doilink/12.2022-28115152/A1

Identification of NSD2 as a Potential Diagnostic and Prognostic Biomarker for Hepatocellular Carcinoma

Wei Zhao1#, Xinyu Xiao1#, Yu Gao1,2, Shanshan Liu3, Xiuzhen Zhang1, Changhong Yang1, Qiling Peng1, Ning Jiang2*, Jianwei Wang1*

Affiliations  

1. School of Basic Medical Science, Chongqing Medical University, Chongqing, China

2. Department of Pathology, Chongqing Medical University, Chongqing, China

3. Department of Hepatobiliary Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China

# These authors contributed equally to this work

* Corresponding Author


Important Dates  

Date of Submission:   13-Aug-2022

Date of Acceptance:   18-Oct-2022

Date of Publication:   30-Dec-2022

ABSTRACT

Aim: To study the role and mechanism of nuclear receptor-binding domain 2 (NSD2) in the occurrence and development of hepatocellular carcinoma (HCC).

Materials and methods: HCCDB, MERAV, and GEPIA databases were used to explore the expression of NSD2 in HCC/control samples. UALCAN analyzed the association of NSD2 expression with clinicopathological parameters. The HPA and TCGA databases validated the prognostic value of NSD2. The cBioPortal and g: Profiler were used to analyze the effect of NSD2 alterations on HCC.  To further explore its molecular mechanism, GO and KEGG enrichment pathway analyses were carried out in the LinkedOmics database. Immunohistochemistry and Western blot were applied to detect NSD2 expression in HCC/control samples.

Results: NSD2 expression in HCC tissues was distinctly higher than in normal liver tissues and was negatively associated with the overall survival of HCC patients. Cox-regression analysis demonstrated that NSD2 could be regarded as an independent risk factor for patients with HCC. Gene enrichment analysis exhibited that the NSD2-associated signaling pathways in HCC were mainly involved in various cellular processes causing tumorigenesis.

Conclusions: NSD2 plays a key role in the progression of HCC and might be used as a new diagnostic and prognostic biomarker.

Keywords: Hepatocellular carcinoma, nuclear receptor-binding domain 2, biomarker, diagnosis, prognosis


INTRODUCTION

Primary liver cancer is one of the most common cancers and the fourth leading cause of cancer-related deaths in the world, and hepatocellular carcinoma (HCC) consists of 90% of all liver cancers.[1] As early clinical symptoms of HCC are not obvious, most patients have already been at an advanced stage when they are diagnosed. Due to the high invasiveness and high mortality of HCC, most patients could survive for only 2~3 months after definite diagnosis.[2] Although current screening methods using biomarkers in HCC such as alpha-fetoprotein (AFP), des-γ-carboxy prothrombin (DCP), and so on are gradually improving, their early detection rate and specificity are still low.[3],[4] Despite improvements in the treatment strategies of HCC nowadays (resection, chemotherapy, transplantation), its prognosis is still not satisfactory, with a 5-year overall survival rate of less than 20%. While the postoperative recurrence rate is greater than 70%.[5],[6] Therefore, it is imperative to explore some novel biomarkers for early diagnosis and prognosis of HCC.

So far, many studies have shown that epigenetics, which refers to diverse and reversible chemical modifications on DNA or histones, play an important role in the development of tumors and is a well-known target for therapeutic intervention.[7],[8],[9] In recent years, the nuclear receptor-binding domain (NSD) family has become a research hotspot because of its epigenetic stability and other characteristics such as histone methyltransferases and T cell activation.[10] It also plays a crucial role in chromatin regulation, amplification, mutation, and overexpression of tumor-associated genes in cancer, which implies that it is implicated in oncogenesis.[11],[12],[13] Especially, it is worth noting that NSD2, one of the NSD family, has a variety of complex mechanisms and correlates with the progression of many diseases such as multiple myeloma (hematologic malignancy) and Wolf-Hirschhorn syndrome. Thus, NSD2 is also referred to as MMSET (multiple myeloma SET domain) or WHSC1 (Wolf-Hirschhorn syndrome candidate 1).  A few recent studies have reported that NSD2 upregulation is associated with cancer progression.[14],[15] On the other hand, some researches have shown that NSD2 is involved in the regulation of apoptosis and is sensitive to chemotherapy in osteosarcoma cells, and promotes RAS-driven transcription in lung cancer cells.[16],[17] However, little is known about its characteristics and functions as a tumor-associated gene of HCC. Therefore, providing vital information on NSD2’s potential as a viable treatment option for HCC patients is necessary.

In this situation, we seek to comprehensively elucidate the roles of NSD2 in HCC by databases and analyze its expression during HCC development and prognosis. In this study, both bioinformatic analysis and experiments have shown an increased expression of NSD2 in cancer tissues. Also, we have found that the NSD2 expression was negatively correlated with overall survival time in a cohort of 339 HCC patients using The Cancer Genome Atlas (TCGA) database. Additionally, gene enrichment analysis has revealed that NSD2-associated signaling pathways in HCC were especially involved in cellular processes causing tumorigenesis. Eventually, this study demonstrated the involvement of NSD2 in HCC and identified it as a novel and promising biomarker for the diagnosis and prognosis of HCC that will serve to increase the survival rate and reduce the mortality rate further in HCC patients.


MATERIALS AND METHODS

HCCDB

HCCDB (hepatocellular carcinoma database) (http://lifeome.net/database/hccdb) with 15 datasets and high coverage (approximately 4,000 clinical samples) is a free one-stop online resource, especially for HCC study. It has been used to construct a model about global differential gene expression of HCC through a user-friendly interface, and make an analysis of uniformly expressed differential genes across multiple datasets.[18] This tool can be utilized to analyze gene expression and co-expression networks in different types of liver tissues.

GEPIA dataset

GEPIA (Gene Expression Profiling Interactive Analysis), is used to comprehensively analyze 8,587 normal samples and 9,736 tumor samples from cancer genome maps and genotypic tissues (http://gepia.cancer-pku.cn/index.html). And its RNA sequencing expression data were analyzed by using a standard processing pipeline for expression (GTEx - Genotype-Tissue Expression) projects and correlation analysis of gene expressions were performed on a given TCGA expression dataset.[19]

Human protein atlas

The Human Protein Atlas (HPA) (http://www.proteinatlas.org), which is one of the most useful databases for protein research, uses transcriptome and proteomics techniques to study protein expression in different human tissues and organs.[20] Importantly, HPA provides different protein expressions, particularly histopathology in normal and tumor tissues/organs. Thus, NSD2 expression at the protein level in normal and cancerous tissues was assessed in HPA. Subsequently, the sub-localization of NSD2 was also verified in HPA.

UALCAN

UALCAN (http://ualcan.path.uab.edu), an online analytics tool, is used to explore the expression of mRNA and its relationship with different clinical indicators.[21] Its resources are mainly based on clinical data of TCGA. In this study, relationships between NSD2 mRNA level and clinical parameters were investigated through the UALCAN database.

cBioPortal and g: Profiler

The cBio Cancer Genomics Portal (cBioPortal) (http://cbioportal.org), including more than 5,000 tumor samples from 20 types of cancers, is an online analysis website mainly used to explore multidimensional cancer genomics data sets.[22] Neighboring genes of NSD2 were downloaded from cBioPortal in the present study, and subsequently an online tool g:Profiler (http://biit.cs.ut.ee/gprofiler/)  was used to perform Gene Ontology (GO) and  Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.[23]

Kaplan-Meier plotter

The Kaplan-Meier plotter (http://kmplot.com/analysis/) is used for survival analysis. Herein, we have analyzed the prognostic value of NSD2 expression in samples with HCC by this tool.

Linkedomics

LinkedOmics (http://www.linkedomics.org/login.php) database based on resources of 11,158 TCGA patients, is an open online biometric platform.[24] And, we have investigated differentially expressed genes linked to NSD2 in a cohort of HCC (n = 371) from the TCGA database and evaluated the correlation among genes via a Pearson correlation coefficient. Furthermore, the involvement of genes in the pathway of GO elements and KEGG analysis were evaluated.[25]

Western blot

Total protein was extracted from normal liver cell lines and cancer cell lines by RIPA buffer (cat: P0013, Beyotime, China) supplemented with a 1% (v/v) protease inhibitor cocktail. The total proteins boiled were electrophoresed on 10% SDS-PAGE gels and then separated proteins were transferred to PVDF membranes. After blocking by 5% skim milk the membrane was incubated with primary antibody against NSD2 overnight at 4°C. It was then incubated with HRP-conjugated secondary antibody (1:2,000, EarthOx, USA) for 1 h at room temperature. Protein bands on the blot were detected using an enhanced chemiluminescence kit.  β-actin was used as a loading control.

Immunohistochemistry (IHC) staining

HCC tissues and adjacent tissues were collected from the First Affiliated Hospital of Chongqing Medical University after obtaining informed consent. All specimens of HCC patients were fixed with formalin, embedded in paraffin, and sectioned at a thickness of 3 μm. The sections were baked at 60°C for 1 h, dewaxed with xylene, and dehydrated with gradient alcohol. After antigen repair, the slides were treated overnight at 4°C with commercial rabbit polyclonal antibody against NSD2 (cat: A7938, ABclonal, China) at a dilution of 1:150, and all the remaining steps were performed according to the instructions provided with the OriGene (OriGene Technologies) kit. Finally, all slices were stained by 3, 3-diaminobenzidine (DAB) (cat: BL732A, Biosharp, China) and hematoxylin, sealed by neutral gum, and then observed under the light microscope. NSD2 staining intensity was scored as follows: no staining, 0; weak staining, 1; and strong staining, 2. Meanwhile, the percentage of positive tumor cells was scored as follows: 1%~25% as 1; 26%~50% as 2; 51%~75% as 3; and 76%~100% as 4. The final score of each section was calculated by multiplying these two scores, and NSD2 expression was determined as low (score < 4) or high (score ≥ 4).[6]

Statistical analysis

Cox proportional hazards model for univariate and multivariate analysis was used. All the data were analyzed by SPSS Version 22.0 (IBM, USA) and GraphPad Prism 8.0 (CA, USA).  P < 0.05 was considered statistically significant.


RESULTS

Differential gene expression profiles and coexpression network of NSD2

Firstly, data from HCCDB was used to detect the mRNA level of NSD2 among different tissue types using a radar chart. The mRNA level of NSD2 expression in normal liver tissues was much lower than that in other normal tissues (liver/other normal: log FC = -1.27), while the mRNA level of NSD2 in liver cancers was much higher (HCC/Adjacent: log FC = 0.34) compared to adjacent normal liver tissues [Figure 1A]. Similarly, 10 out of 11 datasets in the HCCDB database showed that the NSD2 mRNA level in HCC tissues was much higher compared with adjacent normal liver tissues [Figure 1B]. Moreover, the NSD2 co-expression network in normal liver tissues, adjacent tissues, and HCC tissues was analyzed separately, and as expected, their co-expression network patterns were completely different [Figure 1C]. Functional cluster analyses of genes expressed in different tissues showed that co-expressed genes in normal tissues were mainly associated with activities of DNA helicase and histone acetyltransferase, in adjacent tissues mainly with activities of oxidoreductase and dioxygenase, and HCC tissues with the regulation of cell cycle and chromosomes [Supplementary Table 1], which suggested that NSD2 involved different signaling pathways in HCC. Further validation of NSD2 mRNA expression in different databases such as Metabolic gEne RApid Visualizer (MERAV) [Figure 1D] and GEPIA [Figure 1E] resulted in the same conclusion that a significantly higher mRNA level of NSD2 expression was observed in HCC samples. 

Figure 1.
Figure 1. Differential gene expression of NSD2. (A) NSD2 overall expression among different tissues in radar map. (B) Differential expression of NSD2 in different datasets of HCCDB. (C) NSD2 co-expression networks in normal tissues (green), adjacent normal liver tissues (blue) and HCC tissues (red) from the GTEx project. (D-E) NSD2 mRNA expression in cancer tissues and normal tissues in the Metabolic gEne RApid Visualizer (D) and GEPIA (E). * represents P < 0.05.

Association between the mRNA expression of NSD2 and clinicopathological parameters as well as prognosis of HCC

To ascertain which clinicopathological parameters could be related to the level of NSD2, UALCAN was used. It is suggested that the NSD2 mRNA level of HCC patients was distinctly higher than that of healthy people [Figure 2A]. Among clinicopathological parameters, gender [Figure 2B], tumor stage and grade [Figure 2C, 2D], weight, and age [Figure 2E, 2F] of HCC patients were closely related to the mRNA expression of NSD2.

Subsequently, patient prognosis data analyzed by HPA exhibited that higher NSD2 expression had a significantly negative effect on the prognosis of HCC patients (P = 0.00026, median value of NSD2 expression as the cut-off) [Figure 3].

Figure 2.
Figure 2. NSD2 mRNA expression in the UALCAN database. (A) The expression level of NSD2 mRNA in cancer tissues and normal tissues. (B-F) The correlation between the expression level of NSD2 and clinicopathological parameters in HCC patients: gender (B), tumor stage (C), tumor grade (D), weight (E) and age (F). * represents P < 0.05, ** represents P < 0.01, *** represents P < 0.001.

Figure 3.
Figure 3. Correlation between HPA expression and prognostic implication in HCC patients. The HPA databases were used to analyze the association between NSD2 expression and survival.

Univariate and multivariate Cox-regression analysis

To further analyze how NSD2 accompanies multiple factors to influence the prognosis of HCC, univariate and multivariate Cox-regression analyses were conducted. Patient data from TCGA (https://www.cancer.gov) were downloaded [Supplementary Table 2], and data from patients with complete clinical information were collected. Median FPKM value was used to divide the expression of NSD2 into two groups, and results of the chi-square test, Fisher’s exact test, and Spearman correlation analysis [Table 1 and 2] showed that age (P = 0.001), weight (P = 0.007), tumor stage (P = 0.004), and T classification (P = 0.041) influenced the expression of NSD2. These statistically significant parameters were further analyzed by univariate and multivariate Cox-regression analysis to determine whether NSD2 is an independent risk factor for the prognosis of HCC patients. As shown in Table 3, univariate Cox-regression analysis showed that compared with HCC patients with low NSD2 expression, HCC patients with high NSD2 expression had a significantly increased risk of death (P < 0.001). Multivariate Cox-regression analysis showed that expression of NSD2 may be a related factor to poor survival. Consequently, all these suggested that a high level of NSD2 could be an independent risk factor for prognosis in HCC.

Table 1.
Table 1. The Univariate chi-square and Fisher exact test of NSD2 and clinical parameters

Table 2.
Table 2. Spearman correlation analysis of NSD2 and clinical parameters

Table 3.
Table 3. Prognostic values of the factors associated with NSD2 in 226 HCC patients

Genomic alterations of NSD2 in HCC

TCGA sequencing data from cBioPortal was used to analyze the genetic alterations of NSD2 in HCC patients. Results showed that NSD2 was mutated in 31 of 360 (9%) patients [Figure 4A], and among these mutations, 6.39% had high NSD2 mRNA expression (23/360), 1.11% had missense mutations (4/360), 0.83% had amplification (3/360), and 0.28% had deep deletion (1/360). Importantly mutation map [Figure 4B] showed that mutation types of NSD2 were all missense mutations (4/4). In addition, Kaplan-Meier analysis revealed that NSD2 alterations were significantly associated with shorter overall survival (OS) [Figure 4C, P = 0.0108]. Next, to explore possible pathways NSD2 is involved in, neighboring genes linked to NSD2 mutations were downloaded from cBioProtal [Supplementary Table 3] and utilized the g: Profiler tool to enrich NSD2 and its top 50 frequently altered neighboring genes. The results showed that altered neighboring genes were mainly located in the microbody and chromosomal region and mainly involved in the cell cycle and chromosome segregation. Additionally, they were also associated with microtubule and ATP binding [Figure 4D and Supplementary Table 4].

Figure 4.
Figure 4. Genetic alterations of NSD2 and their prognostic value in patients with HCC from cBioPortal. (A) Genomic alterations of NSD2 in HCC by oncoprint. (B) Mutation types of NSD2 in HCC patients. (C) The survival curve of HCC patients with (red) and without (blue) NSD2 alterations. (D) The GO functional enrichment and KEGG pathway analysis of NSD2 altered neighboring genes.

Enrichment analyses of NSD2 and its correlated gene expressions in HCC

The genes negatively and positively correlated with NSD2 were obtained from mRNA sequencing data of HCC patients in LinkedOmics [Figure 5A]. The top 50 significant genes that were positively and negatively correlated with NSD2 have been listed in the heat map [Figure 5B, 5C, and Supplementary Table 5]. Subsequently, the results of GO elements exhibited that co-expressed genes of NSD2 were mainly located in mitochondria, blood microparticles, ribosome, microbody, and chromosomal region, and mainly involved in fatty acid metabolic process, mitochondrial gene expression, acute inflammatory response, cellular amino acid metabolic process, chromosome remodeling, etc.  Additionally, these genes were primarily involved in the structural constituents of ribosomes, electron transfer activity, histone binding and lyase activity. Besides, analysis of the KEGG pathway indicated that these genes were critically involved in the cell cycle, oxidative phosphorylation, ribosome, and phosphatidylinositol signaling pathways [Figure 5D-G]. It is interesting to find that NSD2 could regulate most of the genes involved in the pathway of the cell cycle. Cell cycle related genes regulated by NSD2 in HCC have been listed in Supplementary Table 6, while the signal pathway of the cell cycle in KEGG is shown in Figure 6. Therefore, we surmise whether NSD2 participates in the progression of HCC by regulating the cell cycle of tumor cells.

Figure 5.
Figure 5. Differentially expressed genes correlated with NSD2 in HCC from LinkedOmics. (A) Volcano plot of genes correlated with NSD2 by Pearson’s test analysis; red (positive), green (negative). (B, C) Heat maps of the top 50 genes that were positively and negatively correlated with NSD2 in HCC. (D-G) The significantly enriched GO annotations and KEGG pathway of genes co-expressed with NSD2 in HCC: (D) cellular components, (E) biological processes, (F) molecular functions and (G) KEGG pathway.

Figure 6.
Figure 6. Cell cycle related signaling pathway regulated by the NSD2 in HCC.

Verification of NSD2 expression at the protein level in HCC tissues and cell lines

IHC images in the HPA database were analyzed to detect the expression of NSD2 in HCC tissues. The results showed that the expression of NSD2 in HCC tissues was higher than that in normal tissues, while it’s cell fluorescent staining indicated that subcellular localization of NSD2 was mainly enriched in the nucleus (eg. U-251 MG, U-2 OS, and A-431) as shown in Supplementary Figure 1. Moreover, the clinical HCC tissues and adjacent tissues were applied to verify the expression level of NSD2 by IHC, and results displayed that NSD2 expression in HCC tissues was significantly higher than that in paired adjacent tissues (P < 0.05, as shown in Figure 7A and Figure 7B, the information of fifteen patients as shown in Supplementary Table 7).  In addition, the level of NSD2 in normal hepatic cell line LO2 and HCC cell lines HepG2 and Hep3B was further analyzed by western blot. The results were consistent with the above mentioned results where the level of NSD2 was significantly higher in HepG2 and Hep3B than in LO2 cells [Figure 7C].

Figure 7.
Figure 7. Verification of NSD2 expression at the protein level in HCC. (A) Representative IHC images of NSD2 expression in HCC tissues and paired adjacent tissues. Magnification: left, ×10; right, ×40. (B) The heat map of NSD2 protein expression in 15 cancer tissues and paired adjacent tissues by the IHC scoring described in the Method. The color change from green to red represents increase in the gradual scoring. (C) Western blot of NSD2 and β-actin in LO2, HepG2 and Hep3B cell lines.


DISCUSSION

HCC is one of the most aggressive and deadly cancer, as there is no reliable early detection method and its prognosis is very poor.[26] Although more and more molecular biomarkers are considered specific targets for diagnosis, treatment, and prognosis of HCC, the current early detection rate and evaluation of prognosis are still unsatisfactory.[4],[27],[28] Therefore, it is crucial to find a new reliable biomarker the for diagnosis and prognosis of HCC. NSD2 has been reported as a significant gene linked to cancer with its overexpression by regulating apoptosis and sensitivity to chemotherapy in individual studies. However, little is known about its characteristics and functions as a tumor-associated gene of HCC. In this study, it is demonstrated that NSD2 expression in HCC tissues was significantly higher than that in normal liver and adjacent tissues, indicating that NSD2 may be involved in HCC tumorigenesis and could be regarded as a novel biomarker for diagnosis and prognosis in HCC patients.

To identify the role of NSD2 in HCC, the level of NSD2 in HCC was analyzed multiple times in different databases, and the relationship between NSD2 expression and clinicopathological parameters as well as the prognosis of HCC patients was analyzed. Co-expression network of NSD2 was established to mine possible mechanisms where NSD2 participates in the progression of HCC. To further substantiate the conclusion, HCC tissues and HCC cell lines were employed.

The conclusion that an expression of NSD2 was higher in HCC tissues than in normal or adjacent tissues could be drawn from different databases. Importantly, the IHC results of our clinical samples also showed a higher level of NSD2 in HCC. Additionally, co-expressed molecules of NSD2 in adjacent tissues are involved in oxidation-reduction process, while the co-expressed molecules of NSD2 in HCC tissues were related to the dysregulation of cell cycle and chromosomes, resulting in excessive cell proliferation of tumor cells. Moreover, survival analysis revealed that NSD2 expression was negatively correlated with the survival of patients. All these results indicated that NSD2 may be involved in HCC tumorigenesis. Interestingly, we found that the higher expression of NSD2 was observed in female patients rather than males, so it is supposed that NSD2 involved in HCC may be associated with the level of sex hormone-related signaling pathway. Some studies have reported that estrogen receptors (ERs) regulated metabolic pathways to promote tumorigenesis and NSD2 facilitated the expression of Erα through BET protein BRD3/4 in breast cancer.[29],[30] Besides, we found that the expression of NSD2 was also associated with the age of patients,  which was in accord with Tanaka H.’s report that NSD2 impacted cell aging by regulating the expression of histone H3 lysine 36 trimethylation (H3K36me3) as well as cell cycle-related genes in a retinoblastoma protein (RB)-mediated manner.[31] Apart from that, the influence of body weight on the expression of NSD2 reminded us of its relationship with fat metabolism. Zhuang et al. have shown that depletion of NSD2-mediated H3K36 methylation prevented induction of the master adipogenic transcription factor-peroxisome proliferator-activated receptor-γ (PPARγ).[32] Here, one important thing to note is that all cases were included when we ascertain which clinicopathological parameters could be related to the level of NSD2 using UALCAN database [Figure 2], but the cases with incomplete information were excluded from multivariate Cox-regression analysis [Table 1 and 2]. Since the sample size was small, it may cause statistical variation.  Like other tumor-associated genes, NSD2 expression generally increased as HCC advanced [Figure 3, Table 1 and 2]. However, specifically at cancer stage IV the expression of NSD2 was unexpectedly decreased. There might be two reasons for this phenomenon: (1) since the patient is in the end stage and the condition is related to the liver which is a metabolic organ, gene expression level, in general, gets reduced which could be the cause of NSD2 reduction. (2) Compared with other stages, the sample size of stage IV was too small (only 6 cases). Lastly, univariate and multivariate Cox-regression analysis revealed that NSD2 expression had a significant influence on the survival of HCC patients. NSD2 is therefore reasonably confirmed to be one of the variables affecting the prognosis of HCC.

Evidence has shown that genetic alterations and dysregulated amplification play critical roles in the development of tumors.[33],[34],[35] The intrinsic carcinogenesis mechanism of NSD2 in HCC was explored by analyzing genetic alterations of NSD2. Our results identified many NSD2 genetic mutations in HCC tissues, which were significantly related to patient survival, and, interestingly, mutation types of NSD2 were all missense mutations. Swaroop et al. found a glutamic acid to lysine mutation at residue 1099 (E1099K) in NSD2 in childhood acute lymphocytic leukemia (ALL), which reduced cell apoptosis and enhanced proliferation, clonogenicity, adhesion, and migration.[36] Next, functional enrichment analysis in this study suggested that coexpressed genes with NSD2 were generally involved in cell cycle-related and metabolic processes, which was consistent with previous studies.[37],[38],[39] The identification of these pathways provide new strategies for the treatment and intervention of HCC patients with NSD2 dysfunction, which will also become the direction of our further research.  

In summary, this study provides multi-level demonstrations for identifying the key role of NSD2 in carcinogenesis and its potential as a diagnostic and prognostic biomarker for HCC. Nevertheless, as exact mechanism of NSD2 involvement in HCC is still unclear, our future work will focus on this scientific question to confirm whether NSD2 could be utilized as a clinically useful biomarker.

 

FINANCIAL SUPPORT AND SPONSORSHIP

The authors have no financial support and sponsorship.

 

CONFLICTS OF INTEREST

The authors declare no competing financial interest.

 

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

HCC tissues and adjacent tissues were collected from the First Affiliated Hospital of Chongqing Medical University after obtaining ethics approval.

 

AUTHOR CONTRIBUTIONS

Wei Zhao, Ning Jiang and Jianwei Wang conceived and designed this study. Wei Zhao, Shanshan Liu and Changhong Yang collected and analyzed the relative data. Xiuzhen Zhang collected the clinical samples. Xinyu Xiao and Yu Gao performed the IHC experiments and Western blot. Wei Zhao drafted the article. Jianwei Wang and Qiling Peng revised the manuscript critically for important intellectual content. All the authors read and approved the final manuscript.


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SUPPLEMENTARY FIGURES AND TABLES

Supplementary Figure 1.
Supplementary Figure 1. Expression of NSD2 in clinical HCC tissues from HPA database. (A) Representative IHC images from the HPA database with the NSD2 antibody: CAB068246. (B-E) NSD2 protein subcellular localization in U-251 MG, U-2 OS and A-431cell lines. Green represents NSD2 protein and red represents microtubules.

Supplementary Table 1.
Supplementary Table 1. The functional cluster analysis of the co-expressed genes in different tissues

Supplementary Table 2.
Supplementary Table 2. The clinicopathological parameters of patients from TCGA

Supplementary Table 3.
Supplementary Table 3. The list of neighboring genes related to NSD2 mutations

Supplementary Table 4.
Supplementary Table 4. Enrichment analysis of NSD2 and its frequently altered neighboring genes

Supplementary Table 5.
Supplementary Table 5. The list of top 50 significant genes that were positively and negatively correlated with NSD2

Supplementary Table 6.
Supplementary Table 6. Cell cycle related genes regulated by the NSD2 in HCC

Supplementary Table 7.
Supplementary Table 7. The complete information of fifteen patient samples from the First Affiliated Hospital of Chongqing Medical University


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Yasuka Azuma1,2, Masako Mizuno‑Kamiya3, Eiji Takayama1, Harumi Kawaki1, Toshihiro Inagaki4, Eiichi Chihara2, Yasunori Muramatsu5, Nobuo Kondoh1


“Eating” Cancer Cells by Blocking CD47 Signaling: Cancer Therapy by Targeting the Innate Immune Checkpoint

Yi‑Rong Xiang, Li Liu


Glycosylation is Involved in Malignant Properties of Cancer Cells

Kazunori Hamamura1, Koichi Furukawa2


Biomarkers in Molecular Epidemiology Study of Oral Squamous Cell Carcinoma in the Era of Precision Medicine

Qing‑Hao Zhu1*, Qing‑Chao Shang1*, Zhi‑Hao Hu1*, Yuan Liu2, Bo Li1, Bo Wang1, An‑Hui Wang1


I‑Kappa‑B Kinase‑epsilon Activates Nuclear Factor‑kappa B and STAT5B and Supports Glioblastoma Growth but Amlexanox Shows Little Therapeutic Potential in These Tumors

Nadège Dubois1, Sharon Berendsen2, Aurélie Henry1,2, Minh Nguyen1, Vincent Bours1,
Pierre Alain Robe1,2


Suppressive Effect of Mesenchymal Stromal Cells on Interferon‑g‑Producing Capability of Spleen Cells was Specifically Enhanced through Humoral Mediator(s) from Mouse Oral Squamous Cell Carcinoma Sq‑1979 Cells In Vitro

Toshihiro Inagaki1,2, Masako Mizuno‑Kamiya3, Eiji Takayama1, Harumi Kawaki1, Eiichi Chihara4, Yasunori Muramatsu5, Shinichiro Sumitomo5, Nobuo Kondoh1


An Interplay Between MicroRNA and SOX4 in the Regulation of Epithelial–Mesenchymal Transition and Cancer Progression

Anjali Geethadevi1, Ansul Sharma2, Manish Kumar Sharma3, Deepak Parashar1


MicroRNAs Differentially Expressed in Prostate Cancer of African‑American and European‑American Men

Ernest K. Amankwah


The Role of Reactive Oxygen Species in Screening Anticancer Agents

Xiaohui Xu1, Zilong Dang2, Taoli Sun3, Shengping Zhang1, Hongyan Zhang1


Panobinostat and Its Combination with 3‑Deazaneplanocin‑A Induce Apoptosis and Inhibit In vitro Tumorigenesis and Metastasis in GOS‑3 Glioblastoma Cell Lines

Javier de la Rosa*, Alejandro Urdiciain*, Juan Jesús Aznar‑Morales, Bárbara Meléndez1,
Juan A. Rey2, Miguel A. Idoate3, Javier S. Castresana


Cancer Stem‑Like Cells Have Cisplatin Resistance and miR‑93 Regulate p21 Expression in Breast Cancer

Akiko Sasaki1, Yuko Tsunoda2, Kanji Furuya3, Hideto Oyamada1, Mayumi Tsuji1, Yuko Udaka1, Masahiro Hosonuma1, Haruna Shirako1, Nana Ichimura1, Yuji Kiuchi1


The Contribution of Hexokinase 2 in Glioma

Hui Liu1, Hongwei Yang2, Xin Wang3, Yanyang Tu1


The Mechanism of BMI1 in Regulating Cancer Stemness Maintenance, Metastasis, Chemo‑ and Radiation Resistance

Xiaoshan Xu, Zhen Wang, Nan Liu, Pengxing Zhang, Hui Liu, Jing Qi, Yanyang Tu


A Multisource Adaptive Magnetic Resonance Image Fusion Technique for Versatile Contrast Magnetic Resonance Imaging

Lei Zhang1,2, Fang‑Fang Yin1,2,3, Brittany Moore1,2, Silu Han1,2, Jing Cai1,2,4


Senescence and Cancer

Sulin Zeng1,2, Wen H. Shen2, Li Liu1


The “Wild”‑type Gastrointestinal Stromal Tumors: Heterogeneity on Molecule Characteristics and Clinical Features

Yanhua Mou1, Quan Wang1, Bin Li1,2


Retreatment with Cabazitaxel in a Long‑Surviving Patient with Castration‑Resistant Prostate Cancer and Visceral Metastasis

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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