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An investigation of 6-Shogaol effects on MCF7 cell lines through a systems biology approach

Abstract

Introduction

In the literature, to investigate hormonal mechanisms of cell growth of patients with breast cancer (BC), as the second most common cause of death in the world, the researchers frequently used MCF-7 cell lines. And, identifying the functional mechanisms of therapeutics agents as new cancer inhibitors is still unclear.

Methods

We used the NCBI-GEO dataset (GSE36973) to study the effects of 6-Shogaol on MCF-7 cell lines commonly used for more than 45 years in several studies. The pre-processing and post-processing stages were carried out for the target samples to identify the most significant differentially expressed genes between two MCF-7 with and without treated by 6-Shogaol. Furthermore, various analyses, including biological process and molecular function from the DAVID website, the protein–protein interaction (PPI) network, gene-miRNA, gene-transcription factor, gene-drugs, and gene-diseases networks, statistically significant assoications with clinical features and survival rates were conducted.

Results

The initial outcomes revealed thirty significant DEGs. Among which the approach resulted in eleven upregulated and nineteen downregulated genes. Over-expression of TRADD and CREB3L1 and low-expression of KIF4A and PALMD were substantial in the TNF signaling pathway. Moreover, hsa-mir-16-5p and hsa-mir-124-3p were inhibitors of breast cancer growth.

Conclusion

The fact that some of genes are associated with survival rates as well as various clinical features including disease stages, it can be deduced that the 6-Shogaol treatment on MCF7 cell lines at the genome level shows inhibition functionalities of the herbal medicine in breast cancer at early stages and pave the way in developing new therapeutic agents.

Introduction

Ginger (Zingiber officinale Roscoe (Zingiberaceae)) as one of the most frequently used condiments for enhancing the flavor of the food and confectionery industry can also be of traditional herbal medicine covering a diverse spectrum of diseases [1]. Shogaols are a group of natural dehydrated compounds derived from dried ginger rhizomes that have several biological and pharmacological effects such as anti-oxidative, anti-inflammatory, and anti-cancer properties [2]. The variants of Shogaols are related to the length of the alkyl chain, among which 6-Shogaol [1-(4-hydroxy-3-methoxyphenyl)-4-decen-3-one] is the most potent anti-oxidant [3]. The researchers reported successful therapeutic outcomes of 6-Shogaol for colorectal and breast cancer in some available literature researches [4, 5].

Breast cancer is the second most common leading cause of death in women globally, according to the WHO cancer reports for 2018 that stand after lung cancer (i.e., https://www.who.int/news-room/fact-sheets/detail/cancer). Various in vitro studies presented the inhibitory activity of 6-Shogaol on blood-borne, colon, breast, and skin carcinoma cells [6]. Additionally, the anti-cancer effect of 6-Shogaol has been reported by Gan et al. for MCF‐7 cell lines and animal models [7]. On the other hand, various stages of the breast cancer is of interest of the researchers considering the diagnosis and treatment of the disease at early stages that may beneficial for the patients covering more than 200 studies from Scopus database in 2021. However, the gaps still are available between the computational and biological sciences to understand the state-of-art of molecular functionalities and cellular mechanisms of breast cancer.

The applications of different computational methodologies, among which systems biology and bioinformatics have played vital roles in providing potent biomarkers for diagnosis and prognosis of a specific disease, have frequently utilized on “OMICS” datasets. Various aspects of systems biology approaches may cover the terms metabolomics, genomics, and proteomics. The systems biology techniques can be beneficial for pharmacological sciences. And, the reports showed pharmacological and toxicological effects of compounds assessed for an ailment [8, 9]. For instance, systems pharmacology research revealed the inhibitory effects of three compounds from the ginger family in stroke risks [10]. So, investigating the impact of 6-Shogaol on MCF7 cell lines through the systems biology approach might be of help for those researchers in the field of drug design and discovery to plan for future novel therapeutic agents.

To reach this aim, we have accomplished a thorough systems biology procedure to identify the potential genes differentially expressed between MCF7 cell lines and the corresponding treatment samples with 6-Shogaol. Moreover, the investigation will extend the coverage for an in-depth understanding of involved signaling pathways, while 6-Shogaol is the recommendable treatment for MCF7 cell lines. Finally, the outcomes will be demonstrated and validated through different methodologies in terms of computational, biological aspects, clinical attributes (e.g., TNM stages) and assessment of survival rates.

Materials and methods

Data sources

We downloaded the GSE36973 dataset from The publicly available GEO database of the National Center for Biotechnology Information NCBI-GEO database (i.e., https://www.ncbi.nlm.nih.gov/geo/). Its platform was the GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]. The dataset, comprised 4 MCF-7 cells treated with 10uM 6-Shogaol for (n = 2; GSM907714 and GSM907715) and control (n = 2; GSM907712 and GSM907713).

Differentially expressed genes (DEGs) between two types of tissues

The BRB-ArrayTools (v4.6.0, stable version) developed by Dr. Richard Simon and his team was employed to identify significant differentially expressed genes between treated and control MCF-7 cell lines. The built-in pre-processing procedure included disabling the filtering out parameters of the genes, and quantile normalizing of the imported genes as well as finally annotating the genes by “pd.hugene.1.0.st.v1” R package [11]). The “gcrma” R package was the algorithm to determine the target gene expressions based on their probe intensities. Finally, the significant DEGs could be identifiable by comparing the two classes of samples by setting the threshold values of 10,000 and 1 for univariate permutation tests and the fold change.

Gene ontology and functional enrichment analyses

For assessment of GO (gene ontology), biological processes, cellular components, and molecular functions as well as KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway analysis, DAVID v. 6.8 (Database for Annotation, Visualization, and Integrated Discovery) available at http://david.abcc.ncifcrf.gov/summary.jsp was used [12, 13].

Construction of protein–protein interaction, gene-disease, gene-drug networks

The NetworkAnalyst 3.0 freely available at https://www.networkanalyst.ca was used to construct the generic protein–protein interaction (PPI) network using the STRING database among the identified significant DEGs [14, 15]. Moreover, the relationships of target genes with potential drugs and diseases were of the research interest. Finally, we extracted the efficient subnetwork from the first-order constructed PPI network based on Prize-collecting Steiner Forest (PCSF) algorithm, denoted by the minimum network for illustration of the generated figures [16].

Statistical survival analysis of significant genes

For the purpose of exploring the effects of genes on survival rates, the Kaplan–Meier plotter (http://kmplot.com/analysis/) is an online tool that uses n = 4934 and n = 1880 patients with relapse-free (RFS) and overall survival (OS) data from the TCGA database on breast cancer [17]. It is possible to determine the relationship between a tumor's unique gene and its patients' overall survival time using an online KM plotter database built on the TCGA data. The p-value and 95% confidence interval for the hazard ratio were set to 0.05 and 95%, respectively, as the default settings.

Statistical analysis of clinical features

796 breast cancer patients and 96 healthy women are included in the TCGA breast cancer study (TCGA-BRCA). Using the level 3 mRNA sequencing data and clinical information, further analysis into major DEGs in FireBrowse (http://firebrowse.org) was conducted in terms of years to birth, pathological TNM stages, radiation therapy, histological type, number of lymph nodes, and race [18]. The significance level was set at Q values less than 0.3 and p-value less than 0.05 for the three statistical tests of Spearman correlation, Wilcoxon, and Kruskal Wallis.

Results

After pre-processing and post-processing steps performed on the GEO dataset (i.e., GSE36973) using the BRB-ArrayTools, the outcomes showed eleven up-regulated and nineteen down-regulated differentially expressed genes (DEGs) as listed in Table 1 along with their gene expression levels shown in Fig. 1.

Table 1 List of thirty significant DEGs analyzed from BRB-ArrayTools
Fig. 1
figure 1

The gene expression levels for the identified DEGs

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8, a functional annotation tool to understand the biological values of genes. The involved BP and MF are GO:0010939 regulation of necrotic cell death dealt with changes in morphological cell volume and GO:0003724 ATP-dependent RNA helicase activity with negative control, respectively. The KEGG pathway enrichment analysis represents the involvement of two up-regulated genes in the TNF signaling pathway. The genes include TRADD-Tumor necrosis factor receptor type 1-associated DEATH domain and CREB3L1-cAMP Responsive Element Binding Protein 3 Like 1.

More details on the pathway is available from https://david.ncifcrf.gov/kegg.jsp?path=hsa04668$TNF%20signaling%20pathway&termId=550028766&source=kegg.

The protein–protein interaction analyzed explicitly for breast cancer tissues proposes that KIF4A (Kinesin Family Member 4A) and PALMD (Palmdelphin) have been interconnected with other nine genes (i.e., ARL2BP, SHMT2, TRADD, SH3KBP1, NXF1, APP, ARL6IP6, EED, and RPS23) as illustrated in Fig. 2. Moreover, the construction of the miRNA-Gene network (Fig. 3) shows the highest degree of interactions with hsa-mir-16-5p and hsa-mir-124-3p functioning as suppressors of breast cancer with degree values of 5 and 4, respectively. The network analysis of transcription factors (TFs) and genes in molecular biology reveals the TFs including MRPL54 (with degree 12), CREB3L1 (with degree 12), GATAD2A (with degree 8), IRF1 (with degree 9), ATF1 (with degree 8) are associated with controlling the flow of genetic information from DNA to mRNA (Fig. 4). The construction of a minimized network for gene-disease (Fig. 5) relationships are also indicative of the fact that failure to gain weight and small cell carcinoma of the lung is the most potential illnesses occurring in patients with breast cancer. Finally, the gene-drug network assessment (Fig. 6) demonstrates four potent drugs and chemicals (i.e., 1-Methoxy-2-[2-(2-Methoxy-Ethoxy]-Ethane, 7-AMINO-4-METHYL-CHROMEN-2-ONE, L-Proline, and Cyclosporine) associated directly with down-regulated PPIF gene.

Fig. 2
figure 2

PPI network analysis for identified significant DEGs. The relationship between blue and red gene encoded proteins is connected by small circles (e.g., NFX1 is between PALMD and KIF4A)

Fig. 3
figure 3

miRNA-gene network analysis for identified significant DEGs. The identified DEGs are illustrated by circles and associated mi-RNAs are presented by squares

Fig. 4
figure 4

Gene-TFs network analysis for identified significant DEGs The identified DEGs are illustrated by circles and associated transcription factors are presented by squares

Fig. 5
figure 5

Gene-disease network analysis for identified significant DEGs. The identified DEGs are illustrated by circles and associated diseases are presented by squares

Fig. 6
figure 6

Gene-drug network analysis for identified significant DEGs. The identified DEGs are illustrated by circles and associated chemicals are presented by squares

The survival analyses identified one and seven upregulated as well as seven and fiffteen downregulated genes for OS and RFS, respectively (illustrated in Fig. 7 (OS) and Fig. 8 (RFS)) that are statistically significant.

Fig. 7
figure 7figure 7figure 7figure 7

The KM plot analyese for the identififed genes along with their p-value for overall survival rate (note: the other name for AACT is SERPINA3 as listed in the figure and no OS data were available for OR2G2 and C5orf52 genes)

Fig. 8
figure 8figure 8figure 8figure 8

The KM plot analyese for the identififed genes along with their p-value for relapse free survival rate (note: the other names for the genes are listed as YAF9 (YEATS4), bHLHe38 (MYCL), SERPINA3 (AACT), TMPRSS15 (PRSS7) and no RFS data were available for OR2G2 and C5orf52 genes)

According to Table 2 and considering the DEGs identified from the 6-Shogaol treatment on MCF7 cell line as well as the statistical analysis of TCGA-BRCA, 8 and 5 DEGs are found to be associated with older and younger patients, respectively. Additionally, total of 7 significant genes are asscoiated with pathological T stage of the breast carcinoma; on the other hand, 4 genes are involved in pathological N stage and no genes are covered for the M stage. This may be indicative of the fact that 6-shogaol can be only efficient at the early stages of the disease. Furthermore, 3 genes are found to be significant for radiation therapy and 2 genes are related to the higher number of lymph nodes.

Table 2 The outcomes of FireBrowse for assessment of clinical features versus mRNA genes

Discussion

The inhibition of breast cancer among women is an important and challenging issue in the world. Different types of treatments were proposed and tested by outstanding researchers, among which 6-Shogaol showed its effectiveness in inhibiting the breast cancer cell invasion [1, 4, 19]. So, integrated bioinformatics investigation on effects of 6-Shogaol on MCF7 cell lines became the aim of the current research to provide an in-depth systems biology insights in the field. The primary purpose of studies available in the literature is frequently on identifying the significant biomarker genes to be used in diagnosis, prognosis, and designing drugs for the specific diseases, here, we studied 6-Shogaol structure’s effect on MCF7 cell lines. Significant DEGs between MCF7 cell lines treated and untreated by 6-Shogaol have roles in inhibiting the active constituent of ginger on the expression of different genes. Tumor necrosis factor (TNF), also known as TNF-α and a cytokine, has a critical role in several cellular functions such as apoptosis, cell survival, and tumorigenesis progression and inhibition [20, 21].

The expression levels of some proteins, such as TRADD may play a vital role in inducing apoptosis in M cells of MCF7 cell lines [22]. Although the mechanism of TRADD in various types of cancers is still unclear, however, the downregulation of TRADD has been reported in cancers, including acute myeloid leukemia (AML) exact pathogenesis of leukemia by inducing TNF signaling pathway [7]. The previous studies have also shown the low expression level of CREB3L1 in specifically higher grade tumors in breast cancer [23, 24]. Moreover, the upregulation of CREB3L1 frequently exists in small and medium-grade tumors [24]. So, the upregulation outcome of these proteins through 6-Shogaol treatment for MCF7 cell lines will be evidence for its effectiveness in treating the disease. The PPI minimized network analysis for mammary breast tissue revealed the downregulated KIF1A had significant effects in suppressing the activity of PARP-1 (poly (ADP-ribose) polymerase-1) while patients with breast cancer used doxorubicin treatment [25]. Additionally, the expression level of PALMD in grade III breast cancer tumor was significantly upregulated in comparison to normal tissues, and the downregulation of PALMD could be an indicative of the suppressive effect of 6-shogaol on MCF7 cell lines [26]. The outcomes derived from the construction of the miRNA-gene network have shown that the downregulated miR-16 has remained conserved among many types of cancer, including breast cancer [27]. Some research reports presented overexpression of miR-16-5p may act as an inhibitor of breast cancer progression and development, and hence, may play a vital role in the design of novel drugs and therapeutics for patients with breast carcinoma [28, 29]. Similarly, downregulated miR-124-3p involved in various types of cancer, has frequently been reported in the literature; however, the in-depth mechanisms of this gene are still unclear in breast cancer development [30]. And, various researchers reported the inhibitory role of the miR-124-3p gene in breast and gastric cancers progression [31].

Regarding the weight gain, some studies hinted on the fact that the survivors of breast cancer were the major problem in women following their chemotherapy treatment [32, 33]. Additionally, the outcomes of the clinical literature experiments emphasized the direct relationship between small cell lung carcinoma and breast cancer (i.e., breast metastases), demonstrating two case reports for 38- and 66-year-old women [34, 35]. The literature studies confirmed the outcomes of gene-drug network construction for their effective treatments against breast cancer. These work through carbonic anhydrase IX and XII inhibitors, targeting progesterone metabolism, and downregulation of PKM2 [36,37,38]. However, 1-Ethoxy-2-(2-methoxy ethoxy)ethane was also an inhibitor of the Hepatitis C virus (from PubChem registered Patent: US9085587), which might play a role in preventing breast cancer growth as well.

Altogether, in the current study, the effects of 6-Shogaol in inhibiting the cell metastasis in MCF7 cell lines were proposed using the systems biology technique. Furthermore, one may deduce that 6-Shogaol can be successful in treating patients with breast cancer, specifically at early stages of the disease, as a beneficial therapeutic agent that can be the target of assessment in future randomized clinical trials.

Conclusion

Breast cancer is the cause of the death of a large number of women in the world. Determining adequate treatment for BC is a critical problem of drug design and discovery. 6-Shogaol, as one of the potential constituent of ginger (Zingiber officinale), plays a role in breast cancer inhibition, cell apoptosis, and anti-cancer and anti-proliferative activities. In this study, we used systems biology method to demonstrate the 6-Shogaol effect on MCF-7 cell line and provide a computational way for validating the in-vitro results of the literature even for those without experimental confirmations. 6-Shogaol results in high expression of TRADD and CREB3L1in inducing the TNF-α signaling pathway as one of the essential paths of eliminating various types of tumors. Also, downregulation levels of KIF1A and PALMD are indicative of the anti-cancer effects of 6-Shogaol on MCF-7 cells. Additionally, 6-Shogaol could epigenetically affect the miRNA dysregulation. Moreover, the relationship between identified DEGs and clinical features are statistically verified and the OS and RFS survival rates are statistically analysed for the significant genes affected through the 6-Shogaol treatment on MCF7 cell line. This research indicates that the function of 6-Shogaol herbal medicine can be similar to anti-cancer drugs and also can be a potential component of anti-cancer drugs for treating breast cancer specifically at early stages of the cancer development.

Availability of data and materials

Not applicable.

Abbreviations

ASCL1:

Achaete-scute homolog 1

ADH7:

Alcohol Dehydrogenase 7

ARL2BP:

ADP Ribosylation Factor Like GTPase 2 Binding Protein

ARL6IP6:

ADP Ribosylation Factor Like GTPase 6 Interacting Protein 6

BCL2L12:

BCL2 Like 12

BP:

Biological process

CC:

Cellular component

C5orf52:

Chromosome 5 Open Reading Frame 52

CDH12:

Cadherin-12

CREB3L1:

CAMP Responsive Element Binding Protein 3 Like 1

CRELD1:

Cysteine Rich With EGF Like Domains 1

CTBS:

Chitobiase

DAVID:

Database for Annotation, Visualization, and Integrated Discovery

DEG:

Differentially Expressed Genes

DEPDC1B:

DEP Domain Containing 1B

DHX37:

Probable ATP-dependent RNA helicase

EHD2:

EH Domain Containing 2

FAM83D:

Family With Sequence Similarity 83 Member D

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KIF4A:

Kinesin Family Member 4A

KM:

Kaplan–Meier

MF:

Molecular functions

MRPL54:

Mitochondrial Ribosomal Protein L54

MYCL:

MYCL Proto-Oncogene, BHLH Transcription Factor

NCBI:

National Center for Biotechnology Information

OR2G2:

Olfactory Receptor Family 2 Subfamily G Member 2

OS:

Overall survival rate

PALMD:

Palmdelphin

PCSF:

Prize-collecting Steiner Forest

PPIF:

Peptidyl-prolyl cis–trans isomerase F

PPI:

Protein–protein interaction

PSMG1:

Proteasome Assembly Chaperone 1

RFS:

Relapse-free survival rate

RMDN2:

Regulator of Microtubule Dynamics 2

RPS23:

Ribosomal Protein S23

SERPINA3:

Serpin Family A Member 3

TDRD9:

ATP-dependent RNA helicase

TMEM136:

Transmembrane protein 136

TMPRSS15:

Transmembrane Serine Protease 15

TRADD:

TNFRSF1A Associated Via Death Domain

WHO:

World Health Organization

YEATS4:

YEATS Domain Containing 4

ZNF704:

Zinc Finger Protein 704

References

  1. Ray A, Vasudevan S, Sengupta S (2015) 6-Shogaol inhibits breast cancer cells and stem cell-like spheroids by modulation of Notch signaling pathway and induction of autophagic cell death. PLoS ONE 10(9):e0137614

    PubMed  PubMed Central  Article  Google Scholar 

  2. Weng CJ, Wu CF, Huang HW, Ho CT, Yen GC (2010) Anti-invasion effects of 6-shogaol and 6-gingerol, two active components in ginger, on human hepatocarcinoma cells. Mol Nutr Food Res 54(11):1618–1627

    CAS  PubMed  Article  Google Scholar 

  3. Kou X, Wang X, Ji R, Liu L, Qiao Y, Lou Z et al (2018) Occurrence, biological activity and metabolism of 6-shogaol. Food Funct 9(3):1310–1327

    CAS  PubMed  Article  Google Scholar 

  4. Ling H, Yang H, Tan SH, Chui WK, Chew EH (2010) 6-Shogaol, an active constituent of ginger, inhibits breast cancer cell invasion by reducing matrix metalloproteinase-9 expression via blockade of nuclear factor-κB activation. Br J Pharmacol 161(8):1763–1777

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Pan MH, Hsieh MC, Kuo JM, Lai CS, Wu H, Sang S et al (2008) 6-Shogaol induces apoptosis in human colorectal carcinoma cells via ROS production, caspase activation, and GADD 153 expression. Mol Nutr Food Res 52(5):527–537

    CAS  PubMed  Article  Google Scholar 

  6. Lechner JF, Stoner GD (2019) Gingers and their purified components as cancer chemopreventative agents. Molecules 24(16):2859

    CAS  PubMed Central  Article  Google Scholar 

  7. Gan H, Zhang Y, Zhou Q, Zheng L, Xie X, Veeraraghavan VP et al (2019) Zingerone induced caspase-dependent apoptosis in MCF-7 cells and prevents 7, 12-dimethylbenz (a) anthracene-induced mammary carcinogenesis in experimental rats. J Biochem Mol Toxicol 33(10):e22387

    CAS  PubMed  Article  Google Scholar 

  8. Zhao Q, Zhang J-L, Li F (2018) Application of metabolomics in the study of natural products. Nat Prod Bioprospect 8(4):321–334

    PubMed  PubMed Central  Article  Google Scholar 

  9. Sanni DM, Fatoki TH (2017) Computational evaluation of pharmacokinetics and potential protein targets of ginger (Zingiber officinale). J Microbiol Biotechnol Res 7(1):14–17

    CAS  Article  Google Scholar 

  10. Chang T-T, Chen K-C, Chang K-W, Chen H-Y, Tsai F-J, Sun M-F et al (2011) In silico pharmacology suggests ginger extracts may reduce stroke risks. Mol Biosyst 7(9):2702–2710

    CAS  PubMed  Article  Google Scholar 

  11. Carvalho B (2015) pd.hugene.1.0.st.v1: Platform Design Info for Affymetrix HuGene-1_0-st-v1. R package

  12. da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57

    CAS  Article  Google Scholar 

  13. da Huang W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13

    Article  Google Scholar 

  14. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. NAR 47(W1):W234–W241

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Xia J, Gill EE, Hancock REW (2015) NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc 10(6):823–844

    CAS  PubMed  Article  Google Scholar 

  16. Akhmedov M, Kedaigle A, Chong RE, Montemanni R, Bertoni F, Fraenkel E et al (2017) PCSF: An R-package for network-based interpretation of high-throughput data. PLoS Comput Biol 13(7):e1005694

    PubMed  PubMed Central  Article  Google Scholar 

  17. Lánczky A, Győrffy B (2021) Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res 23(7):e27633

    PubMed  PubMed Central  Article  Google Scholar 

  18. Deng M, Brägelmann J, Kryukov I, Saraiva-Agostinho N, Perner S (2017) FirebrowseR: an R client to the Broad Institute's Firehose Pipeline. Database (Oxford) 2017.

  19. Wu C-H, Hong B-H, Ho C-T, Yen G-C (2015) Targeting cancer stem cells in breast cancer: potential anticancer properties of 6-shogaol and pterostilbene. J Agric Food Chem 63(9):2432–2441

    CAS  PubMed  Article  Google Scholar 

  20. Josephs SF, Ichim TE, Prince SM, Kesari S, Marincola FM, Escobedo AR et al (2018) Unleashing endogenous TNF-alpha as a cancer immunotherapeutic. J Transl Med 16(1):242

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Liu W, Lu X, Shi P, Yang G, Zhou Z, Li W et al (2020) TNF-α increases breast cancer stem-like cells through up-regulating TAZ expression via the non-canonical NF-κB pathway. Sci Rep 10(1):1804

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Burow ME, Weldon CB, Tang Y, Navar GL, Krajewski S, Reed JC et al (1998) Differences in susceptibility to tumor necrosis factor α-induced apoptosis among MCF-7 breast cancer cell variants. Cancer Res 58(21):4940–4946

    CAS  PubMed  Google Scholar 

  23. Denard B, Jiang S, Peng Y, Ye J (2018) CREB3L1 as a potential biomarker predicting response of triple negative breast cancer to doxorubicin-based chemotherapy. BMC Cancer 18(1):813

    PubMed  PubMed Central  Article  Google Scholar 

  24. Ward AK, Mellor P, Smith SE, Kendall S, Just NA, Vizeacoumar FS et al (2016) Epigenetic silencing of CREB3L1 by DNA methylation is associated with high-grade metastatic breast cancers with poor prognosis and is prevalent in triple negative breast cancers. Breast Cancer Res 18(1):12

    PubMed  PubMed Central  Article  Google Scholar 

  25. Wang H, Lu C, Li Q, Xie J, Chen T, Tan Y et al (2014) The role of Kif4A in doxorubicin-induced apoptosis in breast cancer cells. Mol Cells 37(11):812

    PubMed  PubMed Central  Article  Google Scholar 

  26. Gabrovska PN (2012) Gene Expression Analysis in Human Breast Cancer: Griffith University

  27. Zhang H, Yang K, Ren T, Huang Y, Tang X, Guo W (2018) miR-16-5p inhibits chordoma cell proliferation, invasion and metastasis by targeting Smad3. Cell Death Dis 9(6):680

    PubMed  PubMed Central  Article  Google Scholar 

  28. Qu Y, Liu H, Lv X, Liu Y, Wang X, Zhang M et al (2017) MicroRNA-16-5p overexpression suppresses proliferation and invasion as well as triggers apoptosis by targeting VEGFA expression in breast carcinoma. Oncotarget 8(42):72400

    PubMed  PubMed Central  Article  Google Scholar 

  29. Ruan L, Qian X (2019) MiR-16–5p inhibits breast cancer by reducing AKT3 to restrain NF-κB pathway. Biosci Rep 39(8).

  30. Wang Y, Chen L, Wu Z, Wang M, Jin F, Wang N et al (2016) miR-124-3p functions as a tumor suppressor in breast cancer by targeting CBL. BMC Cancer 16(1):826

    PubMed  PubMed Central  Article  Google Scholar 

  31. Liu F, Hu H, Zhao J, Zhang Z, Ai X, Tang L et al (2018) miR-124-3p acts as a potential marker and suppresses tumor growth in gastric cancer. Biomed Rep 9(2):147–155

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Makari-Judson G, Braun B, Jerry DJ, Mertens WC (2014) Weight gain following breast cancer diagnosis: implication and proposed mechanisms. World J Clin Oncol 5(3):272

    PubMed  PubMed Central  Article  Google Scholar 

  33. Vance V, Mourtzakis M, McCargar L, Hanning R (2011) Weight gain in breast cancer survivors: prevalence, pattern and health consequences. Obes Rev 12(4):282–294

    CAS  PubMed  Article  Google Scholar 

  34. Luh S-p, Kuo C, Tsao TC-y (2008) Breast metastasis from small cell lung carcinoma. J Zhejiang Univ Sci B 9(1):39–43

    PubMed  PubMed Central  Article  Google Scholar 

  35. Raber B, Dao T, Howard E, Bredeweg A (2017) Primary small-cell carcinoma of the breast. Proc (Bayl Univ Med Cent) 30(2):200–202

    Google Scholar 

  36. Petreni A, Bonardi A, Lomelino C, Osman SM, AL Othman ZA, Eldehna WM et al (2020) Inclusion of a 5-fluorouracil moiety in nitrogenous bases derivatives as human carbonic anhydrase IX and XII inhibitors produced a targeted action against MDA-MB-231 and T47D breast cancer cells. Eur J Med Chem 190:112112

    CAS  PubMed  Article  Google Scholar 

  37. Singh J, Singh R, Gupta P, Rai S, Ganesher A, Badrinarayan P et al (2017) Targeting progesterone metabolism in breast cancer with L-proline derived new 14-azasteroids. Biorg Med Chem 25(16):4452–4463

    CAS  Article  Google Scholar 

  38. Jiang K, He B, Lai L, Chen Q, Liu Y, Guo Q et al (2012) Cyclosporine A inhibits breast cancer cell growth by downregulating the expression of pyruvate kinase subtype M2. Int J Mol Med 30(2):302–308

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

The authors would like to thank the Research Office of Tabriz University of Medical Sciences for providing support under support scheme (Grant No. 69284)

Funding

It was provided under support scheme Grant No. 69284.

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BS and SA—Conceptualization; EA, BS and SA—Data curation; EA and SA—Formal analysis; EA and BS—Investigation; EA, BS and SA—Methodology; BS—Project administration; BS and SA—Supervision; EA, BS and SA—Roles/Writing—original draft; EA, BS and SA—Writing—review & editing. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Babak Sokouti or Solmaz Asnaashari.

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Amjad, E., Sokouti, B. & Asnaashari, S. An investigation of 6-Shogaol effects on MCF7 cell lines through a systems biology approach. Egypt J Med Hum Genet 23, 60 (2022). https://doi.org/10.1186/s43042-022-00276-y

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  • DOI: https://doi.org/10.1186/s43042-022-00276-y

Keywords

  • 6-Shogaol
  • MCF7 cell line
  • Breast cancer
  • Systems biology
  • Survival rate
  • Clinical features