Analysis of key GO terms and KEGG pathways associated with carcinogenic chemicals

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein-protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

Integrated transcriptomic and metabolomic analyses reveals anthocyanin biosynthesis in leaf coloration of quinoa (Chenopodium quinoa Willd.)

BACKGROUND

Quinoa leaves demonstrate a diverse array of colors, offering a potential enhancement to landscape aesthetics and the development of leisure-oriented sightseeing agriculture in semi-arid regions. This study utilized integrated transcriptomic and metabolomic analyses to investigate the mechanisms underlying anthocyanin synthesis in both emerald green and pink quinoa leaves.

RESULTS

Integrated transcriptomic and metabolomic analyses indicated that both flavonoid biosynthesis pathway (ko00941) and anthocyanin biosynthesis pathway (ko00942) were significantly associated with anthocyanin biosynthesis. Differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were analyzed between the two germplasms during different developmental periods. Ten DEGs were verified using qRT-PCR, and the results were consistent with those of the transcriptomic sequencing. The elevated expression of phenylalanine ammonia-lyase (PAL), chalcone synthase (CHS), 4-coumarate CoA ligase (4CL) and Hydroxycinnamoyltransferase (HCT), as well as the reduced expression of flavanone 3-hydroxylase (F3H) and Flavonol synthase (FLS), likely cause pink leaf formation. In addition, bHLH14, WRKY46, and TGA indirectly affected the activities of CHS and 4CL, collectively regulating the levels of cyanidin 3-O-(3'', 6''-O-dimalonyl) glucoside and naringenin. The diminished expression of PAL, 4CL, and HCT decreased the formation of cyanidin-3-O-(6"-O-malonyl-2"-O-glucuronyl) glucoside, leading to the emergence of emerald green leaves. Moreover, the lowered expression of TGA and WRKY46 indirectly regulated 4CL activity, serving as another important factor in maintaining the emerald green hue in leaves N1, N2, and N3.

CONCLUSION

These findings establish a foundation for elucidating the molecular regulatory mechanisms governing anthocyanin biosynthesis in quinoa leaves, and also provide some theoretical basis for the development of leisure and sightseeing agriculture.

Bioinformatics analysis and experimental validation of tumorigenic role of PPIA in gastric cancer

Gastric cancer (GC) is a malignant tumor with high incidence rate and mortality. Due to the lack of effective diagnostic indicators, most patients are diagnosed in late stage and have a poor prognosis. An increasing number of studies have proved that Peptidylprolyl isomerase A (PPIA) can play an oncogene role in various cancer types. However, the precise mechanism of PPIA in GC is still unclear. Herein, we analyzed the mRNA levels of PPIA in pan-cancer. The prognostic value of PPIA on GC was also evaluated using multiple databases. Additionally, the relationship between PPIA expression and clinical factors in GC was also examined. We further confirmed that PPIA expression was not affected by genetic alteration and DNA methylation. Moreover, the upstream regulator miRNA and lncRNA of PPIA were identified, which suggested that LINC10232/miRNA-204-5p/PPIA axis might act as a potential biological pathway in GC. Finally, this study revealed that PPIA was negatively correlated with immune checkpoint expression, immune cell biomarkers, and immune cell infiltration in GC.

Identification of biomarkers and immune infiltration in acute myocardial infarction and heart failure by integrated analysis

The mortality of heart failure after acute myocardial infarction (AMI) remains high. The aim of the present study was to analyze hub genes and immune infiltration in patients with AMI and heart failure (HF). The study utilized five publicly available gene expression datasets from peripheral blood in patients with AMI who either developed or did not develop HF. The unbiased patterns of 24 immune cell were estimated by xCell algorithm. Single-cell RNA sequencing data were used to examine the immune cell infiltration in heart failure patients. Hub genes were validated by quantitative reverse transcription-PCR (RT-qPCR). In comparison with the coronary heart disease (CHD) group, immune infiltration analysis of AMI patients showed that macrophages M1, macrophages, monocytes, natural killer (NK) cells, and NKT cells were the five most highly activated cell types. Five common immune-related genes (S100A12, AQP9, CSF3R, S100A9, and CD14) were identified as hub genes associated with AMI. Using RT-qPCR, we confirmed FOS, DUSP1, CXCL8, and NFKBIA as the potential biomarkers to identify AMI patients at risk of HF. The study identified several transcripts that differentiate between AMI and CHD, and between HF and non-HF patients. These findings could improve our understanding of the immune response in AMI and HF, and allow for early identification of AMI patients at risk of HF.

Identification and Validation of Immune Markers in Coronary Heart Disease

Background

Coronary heart disease (CHD) is an ischemic heart disease involving a variety of immune factors. This study was aimed at investigating unique immune and m6A patterns in patients with CHD by gene expression in peripheral blood mononuclear cells (PBMCs) and at identifying novel immune biomarkers.

Methods

The CIBERSORT algorithm and single-sample gene set enrichment analysis (ssGSEA) were applied to assess the population of specific infiltrating immunocytes. Weighted Gene Coexpression Network Analysis (WGCNA) was utilized on immune genes matching CHD. A prediction model based on core immune genes was constructed and verified by a machine learning model. Unsupervised cluster analysis identified various immune patterns in the CHD group according to the abundance of immune cells. Methylation of N6 adenosine- (m6A-) related gene was identified from the literature, and t-distributed stochastic neighbor embedding (t-SNE) analysis was used to determine the rationality of the m6A classification. The association between m6A-related genes and various immune cells was estimated using heat maps.

Results

22/28 immune-associated cells differed between the CHD and normal groups, and a significant difference was detected in the expression of 21 m6A-related genes. The proportion of immune-related cells (activated CD4+ T cells and CD8+ T cells) in the peripheral blood of the CHD group was lower than that of the normal group. The immune genes were divided into four modules, of which the turquoise modules showed a significant association with coronary heart disease. Eight hub immune genes (PDGFRA, GNLY, OSMR, NUDT6, FGFR2, IL2RB, TPM2, and S100A1) can well distinguish the CHD group from the normal group. Two different immune patterns were identified in the CHD group. Interestingly, a significant association was detected between the m6A-related genes and immune cell abundance.

Conclusion

In conclusion, we identified different immune and m6A patterns in CHD. Thus, it could be speculated that the immune system plays a crucial role in CHD, and m6A is correlated with immune genes.

Gene Network Mechanism of Zhilong Huoxue Tongyu Capsule in Treating Cerebral Ischemia–Reperfusion

Objective: To investigate the effect of Zhilong Huoxue Tongyu capsule (ZLH) in the treatment of cerebral ischemia–reperfusion injury and determine the underlying molecular network mechanism.

Methods: The treatment effect of Zhilong Huoxue Tongyu capsule (ZLH) was evaluated for cerebral ischemia–reperfusion injury in middle cerebral artery occlusion (MACO) rat, and the underlying molecular network mechanism was explored by using molecular network analysis based on network pharmacology, bioinformatics including protein–protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), as well as molecular docking.

Results: The neurological function of rats in the ZLH group was significantly improved compared to those in the NS group (p = 0.000), confirming the positive effect of ZLH for the treatment of brain ischemia. There were 126 intersecting genes screened in ischemia–reperfusion cerebrum that are associated with several important biological processes, such as lipopolysaccharide, and the most important cell component, such as raft, as well as the most important molecular function pointed as cytokine receptor binding. The most important KEGG signaling pathway was the AGE-RAGE signaling pathway in diabetic complications. Moreover, according to the STRING interaction in the PPI network, 10 hub genes including MAPK14, FOS, MAPK1, JUN, MYC, RELA, ESR1, STAT1, AKT1, and IL6 were selected and exhibited in Cytoscape and molecular docking. Lastly, the relation between PPI, GO, and KEGG was analyzed. These findings indicated that multiple hub network genes have been involved in behavior improvement in cerebral ischemia–reperfusion rats subjected to ZLH treatment.

Conclusion: Zhilong Huoxue Tongyu capsule improves cerebral ischemia–reperfusion and is associated with multiple network gene expressions.

The Role of CEP55 Expression in Tumor Immune Response and Prognosis of Patients with Non-small Cell lung Cancer

Background: With the continuous advancement of diagnostic methods, more and more early-stage Non-small cell lung cancer (NSCLC) patients are diagnosed. Although many scholars have devoted substantial efforts to investigate the pathogenesis and prognosis of NSCLC, its molecular mechanism is still not well explained. Methods: We retrieved three gene datasets GSE10072, GSE19188 and GSE40791 from the Gene Expression Omnibus (GEO) database and screened and identified differentially expressed genes (DEGs). Then, we performed KEGG and GO functional enrichment analysis, survival analysis, risk analysis and prognosis analysis on the selected hub genes. We constructed a protein-protein interaction (PPI) network, and used the STRING database and Cytoscape software. Results: The biological process analysis showed that these genes were mainly enriched in cell division and nuclear division. Survival analysis showed that the genes of CEP55 (centrosomal protein 55), NMU (neuromedin U), CAV1 (Caveolin 1), TBX3 (T-box transcription factor 3), FBLN1 (fibulin 1) and SYNM (synemin) may be involved in the development, invasion or metastasis of NSCLC (P<0.05, logFC>1). Prognostic analysis and independent prognostic analysis showed that the expression of these hub gene-related mRNAs was related to the prognostic risk of NSCLC. Risk analysis showed that the selected hub genes were closely related to the overall survival time of patients with NSCLC. Conclusion: The DEGs and hub genes screened and identified in this study will help us to understand the molecular mechanisms of NSCLC, and CEP55 expression affects the survival and prognosis of patients with NSCLC, and participates in tumor immune response.

An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma

Background: Gliomas are the most common and fatal malignant type of tumor of the central nervous system. RNA post-transcriptional modifications, as a frontier and hotspot in the field of epigenetics, have attracted increased attention in recent years. Among such modifications, methylation is most abundant, and encompasses N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1 methyladenosine (m1A), and 7-methylguanosine (m7G) methylation.

Methods: RNA-sequencing data from healthy tissue and low-grade glioma samples were downloaded from of The Cancer Genome Atlas database along with clinical information and mutation data from glioblastoma tumor samples. Forty-nine m6A/m5C/m1A/m7G-related genes were identified and an m6A/m5C/m1A/m7G-lncRNA signature of co-expressed long non-coding RNAs selected. Least absolute shrinkage and selection operator Cox regression analysis was used to identify 12 m6A/m5C/m1A/m7G-related lncRNAs associated with the prognostic characteristics of glioma and their correlation with immune function and drug sensitivity analyzed. Furthermore, the Chinese Glioma Genome Atlas dataset was used for model validation.

Results: A total of 12 m6A/m5C/m1A/m7G-related genes (AL080276.2, AC092111.1, SOX21-AS1, DNAJC9-AS1, AC025171.1, AL356019.2, AC017104.1, AC099850.3, UNC5B-AS1, AC006064.2, AC010319.4, and AC016822.1) were used to construct a survival and prognosis model, which had good independent prediction ability for patients with glioma. Patients were divided into low and high m6A/m5C/m1A/m7G-LS groups, the latter of which had poor prognosis. In addition, the m6A/m5C/m1A/m7G-LS enabled improved interpretation of the results of enrichment analysis, as well as informing immunotherapy response and drug sensitivity of patients with glioma in different subgroups.

Conclusion: In this study we constructed an m6A/m5C/m1A/m7G-LS and established a nomogram model, which can accurately predict the prognosis of patients with glioma and provides direction toward promising immunotherapy strategies for the future.

An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer’s Disease

Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer’s disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.

Clinical significance and effect of lncRNA BBOX1-AS1 on the proliferation and migration of lung squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have a role in the occurrence and development of lung squamous cell carcinoma (LUSC). lncRNA γ-butyrobetaine hydroxylase 1 (BBOX1)-antisense 1 (AS1) may contribute to disease development. However, there are no studies on the role of BBOX1-AS1 in LUSC to date. In the present study, an in-house gene microarray analysis was performed to detect the differentially expressed lncRNAs and mRNAs between three pairs of LUSC and normal lung tissues. Only one lncRNA, BBOX1-AS1, was differentially expressed in the in-house microarray and The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ArrayExpress databases. Reverse transcription-quantitative PCR (RT-qPCR) was then performed and the original RNA-sequencing data from the TCGA, GEO and ArrayExpress datasets were used to determine the expression and clinical value of BBOX1-AS1 in LUSC. In addition, a Cell Counting Kit-8 assay, cell cycle analysis and scratch assay were performed to explore whether BBOX1-AS1 expression affected the proliferation and migration of LUSC cells in vitro. The results of the RT-qPCR analysis and data obtained from the TCGA database, GEO datasets, in-house gene microarray and standard mean deviation analysis all supported the upregulated expression level of BBOX1-AS1 in LUSC. Furthermore, silencing of BBOX1-AS1 inhibited the proliferation and migration of LUSC cells according to in vitro assays. In addition, the cells were arrested in S-phase after knockdown of BBOX1-AS1. In conclusion, the expression level of BBOX1-AS1 was upregulated in LUSC tissues. BBOX1-AS1 may exert an oncogenic effect on LUSC by regulating various biological functions. However, additional functional experiments should be performed to verify the exact mechanism.

mRNA Network: Solution for Tracking Chemotherapy Insensitivity in Small-Cell Lung Cancer

Background

Small-cell lung cancer (SCLC) has poor prognosis and is prone to drug resistance. It is necessary to search for possible influencing factors for SCLC chemotherapy insensitivity. Therefore, we proposed an mRNA network to track the chemotherapy insensitivity in SCLC.

Methods

Six samples of patients with SCLC were recruited for RNA sequencing. TopHat2 and Cufflinks were used to make differential analysis. Functional analysis was applied as well. Finally, multidimensional validation was applied for verifying the results we obtained by experiment.

Results

This study was a trial of drug resistance in 6 SCLC patients after first-line chemotherapy. The top 10 downregulated genes differentially expressed in the chemo-insensitive group were SERPING1, DRD5, PARVG, PRAME, NKX1-1, MCTP2, PID1, PLEKHA4, SPP1, and SLN. Cell-cell signaling by Wnt (p=6.98E - 21) was the most significantly enriched GO term in biological process, while systemic lupus erythematosus (p=6.97E - 10), alcoholism (p=1.01E - 09), and transcriptional misregulation in cancer (p=0.00227988) were the top three ones of KEGG pathways. In multiple public databases, we also highlighted and verified the vital role of glycolysis/gluconeogenesis pathway and corresponding genes in chemo-insensitivity in SCLC.

Conclusion

Our study confirmed some SCLC chemotherapy insensitivity-related genes, biological processes, and pathways, thus constructing the chemotherapy-insensitive network for SCLC.

Identification of differentially expressed genes using microarray analysis and COL6A1 induction of bone metastasis in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a major cause of cancer-associated mortality worldwide, and bone metastasis is the most prevalent event observed in patients with advanced NSCLC. However, the pathogenesis of bone metastases has not been fully elucidated. In the present study, differentially expressed genes (DEGs) were identified by gene expression microarray analysis of NSCLC tissue samples with or without bone metastases. Subsequently, collagen type 6A1 (COL6A1) was chosen as the target gene through Ingenuity Pathway Analysis and reverse transcription-quantitative (RT-q) PCR validation of the top eight DEGs. COL6A1 was overexpressed or knocked down, and the proliferation and invasion of NSCLC cells was assessed using Cell Counting Kit-8, colony formation and Transwell invasion assays. Additionally, the osteogenic capacity of HOB and hES-MP 002.5 cells was assessed using RT-qPCR, western blotting, Alizarin Red and alkaline phosphatase staining. A total of 364 DEGs were identified in NSCLC tissues with bone metastases compared with NSCLC tissues without bone metastases, including 140 upregulated and 224 downregulated genes. Gene Ontology analysis results demonstrated that the upregulated and downregulated genes were primarily enriched in 'cellular process', 'metabolic process' and 'biological regulation'. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the upregulated genes were primarily enriched in 'cysteine and methionine metabolism', 'oxidative phosphorylation' and 'ribosome', whereas the downregulated genes were primarily enriched in the 'transcriptional misregulation in cancer', 'ribosome' and 'mitophagy-animal' pathways. COL6A1 was highly expressed in NSCLC tissue samples with bone metastases. Functionally, COL6A1 overexpression induced the proliferation and invasion of HARA cells, and its knockdown inhibited the proliferation and invasion of HARA-B4 cells. Finally, it was demonstrated that HOB and hES-MP 002.5 cells exhibited osteogenic capacity, and overexpression of COL6A1 in HARA cells increased the adhesion of these cells to the osteoblasts, whereas knockdown of COL6A1 in HARA-B4 cells reduced their adhesive ability. In conclusion, COL6A1 may serve as a potential diagnostic marker and therapeutic target for bone metastasis in NSCLC.

ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer

BACKGROUND

Gastric cancer (GC) is among the most common forms of cancer affecting the digestive system. This study sought to identify hub genes regulating early GC (EGC) in order to explore their potential for early diagnosis and prognosis of patients.

METHODS

We utilized a publically available dataset from the Gene Expression Omnibus database (GSE55696). Differences between EGC and LGIN with respect to gene expression were compared using the limma software. Identified differentially expressed genes (DEGs) were subjected to gene ontology (GO) and pathway enrichment analyses with the DAVID application, and the STRING website and Cytoscape software were used to construct a protein-protein interaction (PPI) network incorporating these DEGs. This network was in turn used to identify hub genes among selected DEGs, which were analyzed with the Kaplan-Meier Plotter database. In addition, Western blotting, qRT-PCR, immunohistochemistry, and UALCAN were all employed to validate the relationship between the expression of these genes and GC patient prognosis.

RESULTS

A total of 482 DEGs were identified, with GO analyses indicating an increase in the expression of genes linked with the development of cancer. Pathway analyses also indicated that these genes play a role in certain cancer-related pathways. The PPI network highlighted four potential hub genes, of which only ICAM1 was linked to a poor GC patient prognosis. This link between ICAM1 and GC patient outcomes was confirmed via UALCAN, Western blotting, immunohistochemistry, and qRT-PCR.

CONCLUSION

ICAM1 may therefore modulate tumor progression in GC, thus potentially representing a valuable prognostic and diagnostic biomarker of EGC.

Variations of Wnt/β-catenin pathway-related genes in susceptibility to knee osteoarthritis: A three-centre case-control study

Excessive activation of the Wnt signalling pathway in the articular cartilage is demonstrated to be related to the onset and severity of osteoarthritis (OA). However, few studies have investigated the association between variants in Wnt‐pathway‐related genes and the risk of OA by searching Pubmed and EMBASE. Totally, 471 knee OA patients and 532 controls were recruited from three hospitals to evaluate the associations of five genetic variants (rs61735963, rs2908004, rs10795550, rs1799986 and rs1127379) with the risk of knee OA. These polymorphisms were genotyped through polymerase chain reaction and Sanger sequencing. Genetic risk scores (GRSs) were calculated to evaluate the combined effect of these genetic variants. No significant association was found between OA risk and polymorphisms (rs61735963, rs10795550 or rs1127379). However, WNT16 rs2908004 polymorphism was correlated with a decreased risk of OA, especially among females, smokers, non‐drinkers and individuals with age < 60 years or BMI ≥ 25. This SNP was also associated with Kellgren‐Lawrence grade and CRP. Similarly, LRP1 rs1799986 polymorphism decreased the risk of OA among males, smokers, drinkers and individuals with age < 60 years or BMI ≥ 25. TT genotype was more frequent in the group of VAS ≥ 6 versus VAS < 6. A low GRS was positively correlated with a decreased risk of OA. In addition, rs2908004 or rs1799986 polymorphism reduces the expression of WNT16 or LRP1. In conclusion, two SNPs (rs2908004 and rs1799986) are associated with the decreased risk of OA by regulating the Wnt pathway.

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein-protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.