Sage Journals: Discover world-class research

Abstract

BACKGROUND:

Multidrug resistance of Hodgkin’s lymphoma (HL) often results in recurrence. Thus, we aimed to explore the underlying molecular mechanisms of multidrug resistance using bioinformatics strategies.

METHODS:

The gene expression profile was obtained from GEO database. Then, the differentially expressed genes were screened out, and their functional annotations were carried out. Then, gene-signal interaction network was constructed and Connectivity Map (CMAP) analysis was performed.

RESULTS:

A total of 1425 dysregulated genes were screened out, which were mainly enriched in biological items, such as small molecule metabolic, signal transduction, and cell apoptosis. Some survival-related pathways, such as MAPK pathways, apoptosis, and P53 pathway, and several hub genes, such as PRKCA, ACTN1, PIP5K1B, PRKACB, and JAK2, might play key roles in the development of multidrug resistance. Interestingly, felodipine was predicted to be a potential agent overcoming the multidrug resistance.

CONCLUSIONS:

The present study offered new insights into the molecular mechanisms of multidrug resistance and identified a series of important hub genes and small agents that might be critical for treatment of multidrug-resistant HL.

Keywords

Multidrug resistance differentially expressed genes dysfunctional pathway function enrichment analysis Hodgkin’s lymphoma

1. Introduction

Lymphoma is a group of blood cell tumors that originate from lymphocytes, which are classified into two main categories, namely, Hodgkin’s lymphomas (HL) and non-Hodgkin lymphomas (NHL). Lymphomas in adolescents and young adults (AYA) accounting for 22% of all malignant tumors in patients aged 15–24 years (16% for HL and 6% for NHL), respectively [1].

The incidence of HL has gradually risen for decades. Radiotherapy and chemotherapy have been used as the major treatment methods for HL. However, the curable patients would inevitably encounter the risk of long-term toxicity during treatment [2]. Although many patients with HL may be cured by the current regimen of high-dose multi-agent chemotherapy, the treatment causes high risks of later pathologies including secondary malignancies [3]. Moreover, though more than 70% of HL patients are cured after treatment, there are about 30% of patients might experience recurrence [4]. This might be due to the occurrence of multidrug resistance that results from increased drug efflux by elevated expression of drug transporters [5].

The mechanisms of multidrug resistance are complicated. Several factors, such as multidrug resistance proteins and ATP-binding cassette families, are cell membrane transporters that can efflux chemotherapy agents from the cell cytoplasm [6]. To overcome the adverse effects of chemotherapy agents, various targeted therapeutic methods have been developed and several agents have shown more specific effects on cancer cells than chemotherapies. Nevertheless, although these agents have been very useful for cancer treatment, the subsequent presence of acquired resistance has reduced the efficacy of targeted therapies. In fact, the majority of the targeted methods concentrated on any certain molecular target, just ignoring that multidrug resistance has a relationship with aberrant expressions of multiple genes as well as pathways. Hence, it is urgent to explore the resistance-related multiple gene variations with more powerful genome-wide technologies, which may effectively clarify the nature of multidrug resistance and then help explore novel therapeutic strategies.

Microarray is a high-throughput tool for efficiently obtaining global gene expression profiles, which has been widely used for investigating the mechanisms underlying a growing body of diseases, including cancer [7].

In the present study, we aimed to explore the key genes or pathways involved in multidrug resistance of HL by using bioinformatics methods. Public microarray data were downloaded and differentially expressed genes (DEGs) among cells with different degrees of sensitivity to multiple drugs were identified. The functions of DEGs were then assessed by Gene Ontology (GO) annotation, and pathway enrichment. The hub genes were identified through gene-signal interaction network construction. Afterwards, possible agents for overcoming multidrug resistance of HL were predicted by using Connectivity Map (CMAP) database.

2. Methods

2.1 Data source

The gene expression profile of GSE26325 was downloaded from the GEO (Gene Expression Omnibus) public microarray database. This dataset was deposited by Staege et al. [8], in which a total of six specimens, involving 2 lowest sensitivity, 2 intermediate sensitivity, and 2 highest sensitivity HL cell lines, respectively, were available based on GPL96 [HG-U133A] Affymetrix Human Genome U133A Array.

2.2 Identification of differentially expressed genes (DEGs)

The dataset was submitted to Dchip software and GCBI platform (www.gcbi.com.cn) for analysis. The threshold of differentially expressed genes (DEGs) was taken to be $P$ values $<$ 0.05 and $Q$ values $<$ 0.05.

2.3 Functional enrichment analysis of DEGs

The functional enrichment analyses of the DEGs, including gene ontology (GO) function analysis, pathway analysis, and pathway-net analysis were carried out.

GO analysis can organize genes into hierarchical categories and present the gene network according to biological processes, which becomes a tool for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications [9].

Pathway analysis was used to determine the significant DEG-associated pathways according to Kyoto Encyclopedia of Genes and Genomes (KEGG). Fisher’s exact test and chi-squared test was used to select the significant pathways. The threshold of significance was based on P values and false discovery rate (FDR).

Pathway-net analysis was further conducted to select the most significant pathway of the DEGs, which is an interaction network of significant pathways containing DEGs as classified by KEGG database. Each pathway was marked by indegree, outdegree and total degree on the basis of its number of both upstream and downstream interaction pathways. A pathway with a high total degree indicates that this pathway might have a high tendency to regulate or be regulated by other pathways, and hence it might be a key pathway in the biological process.

Table 1
The most significant up-regulated and down-regulated DEGs (Top 10, Lowest-sensitive cells versus highest-sensitive cells)

Probe set ID	Gene symbol	Gene description	Fold change	Gene feature
211734_s_at	FCER1A	Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide	144.9032	Up
202391_at	BASP1	Brain abundant, membrane attached signal protein 1	117.3624	Up
212560_at	SORL1	Sortilin-related receptor, L (DLR class) A repeats containing	75.25109	Up
212063_at	CD44	CD44 molecule (Indian blood group)	64.95454	Up
208334_at	NDST4	N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 4	61.0949	Up
212354_at	SULF1	Sulfatase 1	58.44236	Up
219288_at	C3orf14	Chromosome 3 open reading frame 14	56.35489	Up
202436_s_at	CYP1B1	Cytochrome P450, family 1, subfamily B, polypeptide 1	52.92115	Up
202388_at	RGS2	Regulator of G-protein signaling 2, 24 kDa	45.81115	Up
216598_s_at	CCL2	Chemokine (C-C motif) ligand 2	44.72055	Up
215001_s_at	GLUL	Glutamate-ammonia ligase	$-$ 27.7821	Down
209606_at	CYTIP	Cytohesin 1 interacting protein	$-$ 27.3995	Down
217202_s_at	GLUL	Glutamate-ammonia ligase	$-$ 24.0428	Down
213524_s_at	G0S2	G0/G1switch 2	$-$ 21.7448	Down
39729_at	PRDX2	Peroxiredoxin 2	$-$ 20.759	Down
200648_s_at	GLUL	Glutamate-ammonia ligase	$-$ 19.7925	Down
212698_s_at	10-Sep	Septin 10	$-$ 19.6922	Down
200660_at	S100A11	S100 calcium binding protein A11	$-$ 17.4951	Down
210830_s_at	PON2	Paraoxonase 2	$-$ 17.0655	Down
221111_at	IL26	Interleukin 26	$-$ 16.7009	Down

2.4 Gene-signal network

The gene-signal network was conducted based on the screened DEGs, in which the gene interaction network was constructed. The hub gene was selected based on its association with other genes. The DEGs with more associations with other DEGs indicate their important roles in the Gene-signal interaction network.

2.5 Identification of small molecules that overcome multidrug resistance

The CMAP database (http://www.broad.mit.edu/cmap/) contains whole genomic expression profiles for small active molecular inferences, including 6,100 classes of small molecular interference experiments and 7,056 expression profiles. The DEGs, divided into up- and down-regulated groups were submitted to CMAP for analysis. Compounds with negative connectivity enrichment scores, which imply a mode of action by the matched compounds to reverse the expression direction of query genes, were selected as potential therapeutic agents for multidrug resistance of HL.

3. Results

3.1 Identification of DEGs between Lowest-sensitive and highest-sensitive HL cell lines

The analysis has been conducted as shown in Fig. 1. Based on the public microarray dataset GSE26325, a total of 1425 DEGs between lowest-sensitive and highest-sensitive HL cell lines were screened out, in which 900 genes were upregulated and 525 genes were downregulated. The DEGs are shown to be clustered in Fig. 2 and the top ten DEGs were listed in Table 1.

Figure 1.

The flow chart of the bioinformatics analysis.

Figure 2.

Cluster analysis results for gene expression in lowest-sensitive and highest-sensitive groups. The expression values clustered in the red areas indicate up-regulation while the green areas indicate down-regulation.

Figure 3.

GO Enrichment (a) and KEGG pathway analysis (b).

3.2 Functional annotation and pathway enrichment of DEGs

To investigate the altered biological functions of the DEGs, data were clustered through Gene Ontology (GO) analysis. A $P$ value of $<$ 0.01 was considered significant. GO analysis revealed that the differential genes were enriched in 540 GO terms, mainly including small molecule metabolic process (131 genes), signal transduction (102 genes), negative regulation of apoptotic process (69 genes), apoptotic process (73 genes), positive regulation of transcription from RNA polymerase II promoter (70 genes) and so on.

Next, pathway analysis showed that the DEGs were enriched in 161 pathways. The main pathways included Metabolic pathways (104 genes), PI3K-Akt signaling pathway (43 genes), Epstein-Barr virus infection (33 genes), Pathways in cancer (41 genes), Transcriptional misregulation in cancer (31 genes) and so on.

The top 10 significantly altered GO terms and pathways were presented in Fig. 3.

To learn which pathways play a key role in the multidrug resistance of HL, we further conducted pathway-net analysis to screen the key pathways involved in this process. The analysis revealed that the top 5 pathways are MAPK signaling pathway (41 degree), Apoptosis (31 degree), Pathways in cancer (28 degree), Cell cycle (23 degree), and p53 signaling pathway (18 degree) (Fig. 4). The results showed that the key pathways are important pathways involved in cancer progression.

3.3 Gene signal-network

A signal-net analysis was performed to screen the hub genes that might play key roles in the multidrug resistance of HL. With signal-net, important DEGs were screened out (Table 2 and Fig. 5). The results showed that the genes with top 5 betweenness were PRKCA, ACTN1, PIP5K1B, PRKACB and JAK2.

Figure 4.

Results of the pathway-net analysis.

3.4 CMAP analysis

In order to screen out small molecule drugs that might be capable of reversing the gene alterations, the DEGs were submitted to CMAP, a bioinformatic tool that shows functional connections between small agents and gene expression signatures of diseases, for analysis. The perturbagens from the CMAP were analyzed according to their permutated results, $P$ values, and enrichment scores. As a result, the top 10 negatively related small molecules with highly significant correlations in turn were listed in Table 3. Among these small molecules, felodipine (enrichment score $=$ $-$ 0.995), a member of the 1,4-dihydropyridine class of calcium channel blockers, had the highest negative enrichment score, and therefore may have a relationship with the potential to reverse multidrug resistance.

4. Discussion

In the present study, we used bioinformatic methods to explore the possible molecular mechanisms underlying the multidrug resistance of HL. The results showed that a total of 1425 DEGs were screened out. The GO analysis indicated that these genes were mainly enriched in items such as small molecule metabolic, signal transduction, and cell apoptosis. Then, pathway analysis revealed that some survival-related pathways such as MAPK pathways and P53 pathway may play critical roles in the multidrug resistance of HL. Interestingly, several genes such as PRKCA, ACTN1, PIP5K1B, PRKACB and JAK2, may be the hub genes that might play key roles in this process. Subsequently, using the CMAP tool, a small molecule, felodipine, was predicted to be a potential agent overcoming the multidrug resistance of HL.

Previously, several studies have tried to explore the mechanisms of multidrug resistance of HL. For example, mTOR has been thought to be an important pathway involved in HL multidrug resistance, and thus Everolimus, an inhibitor of mTOR, has been considered as a potential treatment drug for reversing the multidrug resistance [10]. Besides, over-expression of Glutathione-S-transferases has a correlation with multidrug resistance of HL [11]. Moreover, IL-4 and IL-13-mediated STAT6 activation has also been suggested to play a role in multidrug resistance of HL [3]. However, only any certain pathway was considered in each of these published studies. Since HL is a disorder characterized by multi-steps and multiple involved genes and pathways, use of bioinformatics methods for analyzing high throughput data may objectively help find key genes and pathways. To our knowledge, we for the first time used bioinformatics methods looking for the underlying molecular mechanisms of HL multidrug resistance.

Through GO analysis and pathway analysis, we found that some items may have a close association with HL multidrug resistance. For instance, signaling transduction, cell apoptosis, and small molecule metabolic process may be the key mechanisms. The pathway-net analysis further indicated that MAPK and P53 pathway may play critical roles during this process. Evidence showed that MAPK p38 pathway was involved in the multidrug resistance of oral carcinoma [12]. Although this signaling pathway has been studied for more than 25 years, its intricate dynamic control and plasticity regarding drug resistance are still unclear [13]. Thus, MAPK signaling pathway might play a key role in the multidrug resistance of HL. P53 is a tumor suppressor that has been indicated to be mutated in a variety of cancers and has been suggested to have a relationship with drug resistance [14]. Reports showed that up-regulated expression of P53 reversed chemoresistance of osteosarcoma cell lines [15]. In lung cancer, mutant P53 confers chemoresistance to cisplatin through upregulation of Nrf2 expression [16]. Hence, signaling pathways such as MAPK and P53 may be the key pathways involved in the multidrug resistance of HL.

Table 2

Hub genes in the gene-signal network (top 5)

Gene symbol	Gene feature	Betweenness	Indegree	Outdegree	Degree
PRKCA	Up	5404.8774	5	12	17
ACTN1	Up	2986.5833	6	6	12
PIP5K1B	Up	2921.8357	6	5	11
PRKACB	Up	2169.3333	3	12	15
JAK2	Up	2125.7157	14	5	19

Table 3

Predicted therapeutic small molecule agents with potential abilities to overcome multidrug resistance (top 10)

Cmap name	Mean	$n$	Enrichment	Percent non-null
Felodipine	$-$ 0.874	1	$-$ 0.995	100
5224221	$-$ 0.866	2	$-$ 0.994	100
Gefitinib	$-$ 0.754	1	$-$ 0.974	100
5286656	$-$ 0.739	1	$-$ 0.969	100
Celastrol	$-$ 0.714	1	$-$ 0.96	100
Quinostatin	$-$ 0.721	2	$-$ 0.937	100
Oxamic acid	$-$ 0.636	1	$-$ 0.923	100
5152487	$-$ 0.609	1	$-$ 0.909	100
Prestwick-1085	$-$ 0.654	4	$-$ 0.906	100
5114445	$-$ 0.582	1	$-$ 0.893	100

Figure 5.

Gene-signal interaction network diagram of the 5 node genes (proteins) including PRKCA, ACTN1, PIP5K1B, PRKACB and JAK2, which showing more association with other node proteins, indicating they are hub genes.

Through gene-signal network construction, a series of hub genes (proteins) have been shown to form a local network, including PRKCA, ACTN1, PIP5K1B, PRKACB and JAK2, of which, several genes have been implied to be related to cancer development or drug resistance. Over-expression of PRKCA may confer chemoresistance in breast cancer [17] and ovarian cancer [18], and its inhibition results in reversion of the resistance. ACTNs are known to crosslink actin filaments at focal adhesions in migrating cells and thus may play a role in cancer cell motility and invasion [19]. As a key regulator of the actin cytoskeleton, its up-regulation in melanoma may promote the cell invasive abilities [20]. PIP5K1B is an enzyme functionally linked to actin cytoskeleton dynamics that phosphorylates phosphatidylinositol 4-phosphate [PI(4)P] to generate phosphatidylinositol-4,5-bisphosphate [PI(4,5)P2]. Knockdown of PIP5K1B in cells results in abnormal actin cytoskeleton remodeling [21]. Thus, aberrant expression of PIP5K1B has been suggested to play a role in the progression of lung adenocarcinoma [22]. PRKACB is a member of the Ser/Thr protein kinase family, which was also a key effector of the cAMP/PKA-induced signaling transduction involved in various cellular processes, such as cell proliferation and differentiation. Therefore, downregulation of PRKACB might promote lung cancer development [23]. JAK2 is a major component of JAK2/STAT3 signaling pathway, which participates in autophagy-related drug resistance of bladder cancer cells [24]. Inhibition of JAK2/STAT3 could sensitize platinum-resistant cells to carboplatin [25]. Collectively, the above evidence supported the notion that the hub genes might play important roles in the multidrug resistance of HL. Nevertheless, it is still unknown whether these above hub genes can be used as targets for the drug resistance reversion. The data of the present study are preliminary findings. Future evaluations for the potential roles of the hub genes are required.

CMAP is an online tool that profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments [26]. Through this tool, a list of agents that might reverse the DEGs profiles were screened out, of which felodipine, a member of the 1,4-dihydropyridine class of calcium channel blockers, had the highest negative enrichment score and thus it might have the potential to reverse the multidrug resistance of HL. Felodipine has been used to treat hypertension and angina and it has no negative inotropic effects at clinically administered doses [27]. Reports showed that calcium antagonists including felodipine can reverse ABCG2/BCRP-mediated drug resistance and transport in SN-38-resistant HeLa cells [28]. Moreover, in a study regarding cholangiocarcinoma, computational bioinformatics analysis identified that felodipine could induce inverse gene changes to the signature using the CMAP as analyzed in the present study. Then, in their further validation experiment, felodipine was indicated to have a synergistic effect with gemcitabine on cancer cells in vitro and in vivo [29]. Thus, the evidence led us to hypothesize that felodipine might be an effective agent overcoming multidrug resistance of HL.

In conclusion, the study provides preliminary investigation for the mechanisms underlying the multidrug resistance of HL. The DEGs of the multidrug resistance of HL were screened out by computational bioinformatics methods based on microarray data. Then, the aberrant pathways involved in this process were identified. Afterwards, several key hub genes were selected as potential targets for reversing the multidrug resistance of HL. Through the CMAP tool, agents that might have the potential to reverse the drug resistance have been predicted and analyzed. The results of the present study may give a valuable clue for both the basic research and clinical treatment of drug resistant HL. However, future validation experiments are warranted to test the findings.

References

Brugieres

and Brice

, Lymphoma in adolescents and young adults, Prog Tumor Res 43 (2016), 101–14.

Seror

Donadieu

Pacquement

Abbou

Lambilliotte

Schell

Curtillet

Gandemer

Pasquet

Aladjidi

Lutz

Schmitt

Deville

Minckes

Vanier

J.P.

Armari-Alla

Thomas

Gorde-Grosjean

Millot

Blouin

Garnier

Coze

Devoldere

Reguerre

Helfre

Claude

Clavel

Oberlin

Landman-Parker

and Leblanc

, Combined therapy in children and adolescents with classical Hodgkin’s lymphoma: A report from the SFCE on MDH-03 national guidelines, Pediatr Hematol Oncol 33 (2016), 423–437.

Natoli

Lupertz

Merz

Muller

W.W.

Kohler

Krammer

P.H.

and Li-Weber

, Targeting the IL-4/IL-13 signaling pathway sensitizes Hodgkin lymphoma cells to chemotherapeutic drugs, Int J Cancer 133 (2013), 1945–54.

Quddus

and Armitage

J.O.

, Salvage therapy for Hodgkin’s lymphoma, Cancer J 15 (2009), 161–3.

Seebacher

Lane

D.J.

Richardson

D.R.

and Jansson

P.J.

, Turning the gun on cancer: Utilizing lysosomal P-glycoprotein as a new strategy to overcome multi-drug resistance, Free Radic Biol Med 96 (2016), 432–45.

Khamisipour

Jadidi-Niaragh

Jahromi

A.S.

Zandi

and Hojjat-Farsangi

, Mechanisms of tumor cell resistance to the current targeted-therapy agents, Tumour Biol 37 (2016), 10021–39.

Abulwerdi

F.A.

and Schneekloth

J.S.

, Jr., Microarray-based technologies for the discovery of selective, RNA-binding molecules, Methods 103 (2016), 188–95.

Staege

M.S.

Banning-Eichenseer

Weissflog

Volkmer

Burdach

Richter

Mauz-Korholz

Foll

and Korholz

, Gene expression profiles of Hodgkin’s lymphoma cell lines with different sensitivity to cytotoxic drugs, Exp Hematol 36 (2008), 886–96.

Tian

Wang

Guo

Liu

and Teng

, An improved method for functional similarity analysis of genes based on gene ontology, BMC Syst Biol 10 (2016), 119.

10.

Guarini

Minoia

Giannoccaro

Rana

Iacobazzi

Lapietra

Raimondi

Silvestris

Gadaleta

C.D.

and Ranieri

, mTOR as a target of everolimus in refractory/relapsed Hodgkin lymphoma, Curr Med Chem 19 (2012), 945–54.

11.

Bernig

Ritz

Brodt

Volkmer

and Staege

M.S.

, Glutathione-S-transferases and chemotherapy resistance of Hodgkin’s lymphoma cell lines, Anticancer Res 36 (2016), 3905–15.

12.

Utaipan

Athipornchai

Suksamrarn

Chunsrivirot

and Chunglok

, Isomahanine induces endoplasmic reticulum stress and simultaneously triggers p38 MAPK-mediated apoptosis and autophagy in multidrug-resistant human oral squamous cell carcinoma cells, Oncol Rep 37 (2017), 1243–1252.

13.

Rauch

Rukhlenko

O.S.

Kolch

and Kholodenko

B.N.

, MAPK kinase signalling dynamics regulate cell fate decisions and drug resistance, Curr Opin Struct Biol 41 (2016), 151–158.

14.

Hientz

Mohr

Bhakta-Guha

and Efferth

, The role of p53 in cancer drug resistance and targeted chemotherapy, Oncotarget 8 (2017), 8921–8946.

15.

Shen

Choy

Yang

Mankin

Hornicek

and Duan

, p53 overexpression increases chemosensitivity in multidrug-resistant osteosarcoma cell lines, Cancer Chemother Pharmacol 77 (2016), 349–56.

16.

Tung

M.C.

Lin

P.L.

Wang

Y.C.

T.Y.

Lee

M.C.

Yeh

S.D.

Chen

C.Y.

and Lee

, Mutant p53 confers chemoresistance in non-small cell lung cancer by upregulating Nrf2, Oncotarget 6 (2015), 41692–705.

17.

Kim

C.W.

Asai

Kang

J.H.

Kishimura

Mori

and Katayama

, Reversal of efflux of an anticancer drug in human drug-resistant breast cancer cells by inhibition of protein kinase Calpha (PKCalpha) activity, Tumour Biol 37 (2016), 1901–8.

18.

Arrighetti

Cossa

De Cecco

Stucchi

Carenini

Corna

Gandellini

Zaffaroni

Perego

and Gatti

, PKC-alpha modulation by miR-483-3p in platinum-resistant ovarian carcinoma cells, Toxicol Appl Pharmacol 310 (2016), 9–19.

19.

Fukumoto

Kurisu

Yamada

and Takenawa

, alpha-Actinin-4 enhances colorectal cancer cell invasion by suppressing focal adhesion maturation, PLoS One 10 (2015), e0120616.

20.

Shao

Watkins

S.C.

and Wells

, alpha-Actinin-4 is required for amoeboid-type invasiveness of melanoma cells, J Biol Chem 289 (2014), 32717–28.

21.

Bayot

Reichman

Lebon

Csaba

Aubry

Sterkers

Husson

Rak

and Rustin

, Cis-silencing of PIP5K1B evidenced in Friedreich’s ataxia patient cells results in cytoskeleton anomalies, Hum Mol Genet 22 (2013), 2894–904.

22.

Chen

Sun

Zheng

and Xu

, Gene expression profiling analysis of lung adenocarcinoma, Braz J Med Biol Res 49 (2016).

23.

Chen

Gao

Tian

and Tian

D.L.

, PRKACB is downregulated in non-small cell lung cancer and exogenous PRKACB inhibits proliferation and invasion of LTEP-A2 cells, Oncol Lett 5 (2013), 1803–1808.

24.

Ojha

Singh

S.K.

and Bhattacharyya

, JAK-mediated autophagy regulates stemness and cell survival in cisplatin resistant bladder cancer cells, Biochim Biophys Acta 1860 (2016), 2484–97.

25.

Jung

J.G.

Shih

I.M.

Park

J.T.

Gerry

Kim

T.H.

Ayhan

Handschuh

Davidson

Fader

A.N.

Selleri

and Wang

T.L.

, Ovarian cancer chemoresistance relies on the stem cell reprogramming factor PBX1, Cancer Res 76 (2016), 6351–6361.

26.

Putcha

and Silva

J.M.

, Recovering drug-induced apoptosis subnetwork from Connectivity Map data, Biomed Res Int 2015 (2015), 708563.

27.

Navadiya

and Tiwari

, Pharmacology, efficacy and safety of felodipine with a focus on hypertension and angina pectoris, Curr Drug Saf 10 (2015), 194–201.

28.

Takara

Matsubara

Yamamoto

Minegaki

Takegami

Takahashi

Yokoyama

and Okumura

, Differential effects of calcium antagonists on ABCG2/BCRP-mediated drug resistance and transport in SN-38-resistant HeLa cells, Mol Med Rep 5 (2012), 603–9.

29.

Braconi

Swenson

Kogure

Huang

and Patel

, Targeting the IL-6 dependent phenotype can identify novel therapies for cholangiocarcinoma, PLoS One 5 (2010), e15195.

Identification of crucial genes and prediction of small molecules for multidrug resistance of Hodgkin’s lymphomas

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Methods

2.1 Data source

2.2 Identification of differentially expressed genes (DEGs)

2.3 Functional enrichment analysis of DEGs

Table 1 The most significant up-regulated and down-regulated DEGs (Top 10, Lowest-sensitive cells versus highest-sensitive cells)

2.5 Identification of small molecules that overcome multidrug resistance

3. Results

3.1 Identification of DEGs between Lowest-sensitive and highest-sensitive HL cell lines

3.3 Gene signal-network

4. Discussion

References

Table 1
The most significant up-regulated and down-regulated DEGs (Top 10, Lowest-sensitive cells versus highest-sensitive cells)