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Brief Report

Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer

by
Shihori Tanabe
1,*,
Eger Boonstra
2,
Taehun Hong
2,
Sabina Quader
3,
Ryuichi Ono
4,
Horacio Cabral
2,
Kazuhiko Aoyagi
5,
Hiroshi Yokozaki
6,
Edward J. Perkins
7 and
Hiroki Sasaki
8
1
Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
2
Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-0033, Japan
3
Innovation Centre of NanoMedicine (iCONM), Kawasaki Institute of Industrial Promotion, Kawasaki 210-0821, Japan
4
Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
5
Department of Clinical Genomics, National Cancer Center Research Institute, Tokyo 104-0045, Japan
6
Department of Pathology, Kobe University of Graduate School of Medicine, Kobe 650-0017, Japan
7
US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA
8
Department of Translational Oncology, National Cancer Center Research Institute, Tokyo 104-0045, Japan
*
Author to whom correspondence should be addressed.
Genes 2023, 14(11), 2073; https://doi.org/10.3390/genes14112073
Submission received: 20 October 2023 / Revised: 10 November 2023 / Accepted: 10 November 2023 / Published: 13 November 2023
(This article belongs to the Special Issue Small Non-coding RNAs in Normal Physiology, Development and Diseases)
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Review Reports Versions Notes

Abstract

:
The precise mechanism of resistance to anti-cancer drugs such as platinum drugs is not fully revealed. To reveal the mechanism of drug resistance, the molecular networks of anti-cancer drugs such as cisplatin, carboplatin, oxaliplatin, and arsenic trioxide were analyzed in several types of cancers. Since diffuse-type stomach adenocarcinoma, which has epithelial–mesenchymal transition (EMT)-like characteristics, is more malignant than intestinal-type stomach adenocarcinoma, the gene expression and molecular networks in diffuse- and intestinal-type stomach adenocarcinomas were analyzed. Analysis of carboplatin revealed the causal network in diffuse large B-cell lymphoma. The upstream regulators of the molecular networks of cisplatin-treated lung adenocarcinoma included the anti-cancer drug trichostatin A (TSA), a histone deacetylase inhibitor. The upstream regulator analysis of cisplatin revealed an increase in FAS, BTG2, SESN1, and CDKN1A, and the involvement of the tumor microenvironment pathway. The molecular networks were predicted to interact with several microRNAs, which may contribute to the identification of new drug targets for drug-resistant cancer. Analysis of oxaliplatin, a platinum drug, revealed that the SPINK1 pancreatic cancer pathway is inactivated in ischemic cardiomyopathy. The study showed the importance of the molecular networks of anti-cancer drugs and tumor microenvironment in the treatment of cancer resistant to anti-cancer drugs.

1. Introduction

Metallodrugs, including platinum drugs and arsenic trioxide, are widely used as cancer therapeutics [1,2,3,4,5]. Platinum drugs, such as cisplatin and carboplatin, are commonly used for the treatment of a variety of cancers, including bladder, head and neck, lung, gastrointestinal, ovarian, and testicular cancer [6,7]. Arsenic trioxide is also the front-line drug for the treatment of acute promyelocytic leukemia [8,9]. Arsenic trioxide has a long history of medicinal use for thousands of years, despite its toxic effects as a poison [10,11]. Arsenic trioxide is effective for patients with recurrent acute promyelocytic leukemia [12]. An increasing number of patients have been treated with arsenic trioxide; however, the precise mechanism of action is not fully understood [10,11].
The principal mechanism of cisplatin-induced cytotoxicity is the formation of platinum-DNA adducts, for which cellular reactions attenuating the DNA damage are key factors in resistance to cisplatin [13]. Cisplatin treatment causes several toxic side effects, including hepatotoxicity, cardiotoxicity, and nephrotoxicity, which may be based on the nature of cisplatin interacting with DNA and forming covalent adducts with purine DNA bases [6]. The mechanisms of cisplatin resistance include pre-target resistance relating to the reduction of cisplatin uptake, the increase in cisplatin effusion to the extracellular space or the increased sequestration of cisplatin by glutathione, on-target resistance involving molecular damage caused by DNA binding of cisplatin, post-target resistance, including signaling pathways leading to cell death triggered by adducts, and off-target resistance relating to alterations in signaling pathways that interfere with cisplatin-induced proapoptotic events [14,15]. Furthermore, epigenetic mechanisms regulating gene expression in non-coding RNA basis and metabolic processes, including glycometabolism and amino acid and lipid metabolism, are associated with resistance to cisplatin treatment [14,15]. In terms of uptake and efflux of platinum drugs, polymorphisms of transporters influence platinum drugs [7]. Although numerous research studies have examined the mechanism of chemoresistance, the extent to which metallodrugs produce drug resistance has not yet been fully revealed [16,17,18,19].
A study in oral squamous cell carcinoma suggested that microRNA (miRNA)-485-5p targets keratin 17 to regulate oral cancer stemness and chemoresistance through integrin, protein tyrosine kinase 2 (PTK2 or FAK), Src, mitogen-activated protein kinase 1 (MAPK1 or ERK), and the β-catenin pathway [16]. N6-methyladenosine RNA methylation of centromere protein K in cervical cancer promoted stemness and chemoresistance. The activation of Wnt/β-catenin signaling leads to the enhancement of DNA damage repair pathways that are necessary for cisplatin/carboplatin resistance and the epithelial–mesenchymal transition (EMT) that is involved in metastasis [17].
In non-small cell lung cancer, miRNAs play roles in the resistance to platinum-based chemotherapy such as adjuvant cisplatin (CDDP, cis-diammine-dichloro-platinum II) [18]. It has been suggested that miR-129-1-3p, miR-155, miR-200c, the miR-17 family (-17, 20a, 20b), miR-15b, miR-27, and miR-181a are involved in CDDP resistance and the EMT in non-small cell lung cancer [18]. Recent studies demonstrated that mRNA, miRNA, cirRNA, and metabolites were related to the resistance of cisplatin in cancer [20,21,22].
It has been reported that miRNAs are involved in cisplatin response by regulating the EMT [23]. The EMT, a cellular phenomenon in which cell types change from epithelial type to mesenchymal type, is involved in the malignancy of cancers, including proliferation, invasion, and metastasis [24]. The EMT phenotype is regulated by miRNAs and plays important roles in drug resistance that is implicated with cancer stemness [24]. Tumor-derived exosomes containing several miRNAs promote the EMT and cancer-cell invasion [25]. Although the EMT is involved in several signaling pathways, such as the phosphatase and tensin homolog (PTEN)/phosphatidylinositol 3-kinase (PI3K), Wnt/β-catenin, and TGF-β pathways, the precise correlation among the EMT, exosomes, and drug resistance is unknown [25]. The EMT is one of the features of cancer malignancy and treatment resistance [26,27,28,29,30]. Previous studies demonstrated that the EMT is implicated with cisplatin resistance [31,32,33]; however, the precise mechanisms and the underlying networks are not fully understood. In this context, the present study investigated the relationship between metallodrugs and the EMT and tumor microenvironment pathways. Additionally, the molecular networks of carboplatin, cisplatin, and arsenic trioxide were investigated in the study.

2. Materials and Methods

2.1. RNA Sequencing Data Collection

The RNA sequencing data of diffuse- and intestinal-type stomach adenocarcinomas are publicly available in The Cancer Genome Atlas (TCGA) of the cBioPortal for Cancer Genomics database at the National Cancer Institute (NCI) Genomic Data Commons (GDC) data portal [34,35,36,37]. Publicly available data on stomach adenocarcinoma in the TCGA, Stomach Adenocarcinoma (TCGA, PanCancer Atlas) [34,36] were compared between diffuse-type stomach adenocarcinoma, which is genomically stable (n = 50), and intestinal- stomach adenocarcinoma, which has a feature of chromosomal instability (n = 223), in TCGA Research Network publications, as previously described [34,38,39].

2.2. Network Pathway Analysis

Data on intestinal- and diffuse-type stomach adenocarcinomas from the TCGA cBioPortal for Cancer Genomics were uploaded and analyzed through the use of Ingenuity Pathway Analysis (IPA) (QIAGEN Inc., Hilden, Germany) [40]. In the IPA database, causal networks were analyzed and bioprofiler analysis was performed on carboplatin. Causal networks, canonical pathways, and regulatory networks were analyzed in gastric adenocarcinoma data, lung adenocarcinoma data, diffuse large B-cell lymphoma data, and arsenic trioxide treatment data in the IPA database.

2.3. Data Visualization

The results of network analyses of causal networks of cisplatin treatment were visualized with Tableau 2023.3 software (https://www.tableau.com (accessed on 7 November 2023)).

2.4. Statistic Analysis

The RNA sequencing data on diffuse- and intestinal-type stomach adenocarcinomas were analyzed via Student’s t-test. The z-scores of intestinal- and diffuse-type stomach adenocarcinoma samples were compared, and the difference was considered significant at p < 0.00001, following previous reports [38,41]. The activation z-score in each pathway was calculated in IPA to show the level of activation.

3. Results

3.1. Causal Networks of Carboplatin Activity Plot in Cancer Treatment

Platinum drugs were searched with the term “platinum” in Ingenuity Pathway Analysis (IPA), which included carboplatin and cisplatin (Table 1). To reveal the causal network of carboplatin treatment, master regulators in depth 3 were investigated in the carboplatin activity plot in IPA analysis. The analysis in the top z-score of 790 analyses for carboplatin in depth 3 of the master regulators was in diffuse large B-cell lymphoma (DLBCL) (as of 2022) (Figure 1a). In this analysis, N-(furan-2-ylmethyl)-8-(4-methylsulfonylphenyl)-[1,2,4]triazolo[4,3-c]pyrimidin-5-amine (EED226) (5 µM, 24 h), a potent and selective inhibitor of polycomb repressive complex 2 (PRC2) that directly binds to the histone H3 lysine 27 binding pocket of embryonic ectoderm development (EED), a subunit of PRC2, was treated to DLBCL [42]. Bioprofiler analysis on carboplatin identified the relationship between carboplatin and anaplastic lymphoma kinase receptor tyrosine kinase (ALK) mutation-negative CD274 positive non-small cell lung cancer, ALK mutation negative epidermal growth factor receptor (EGFR) sensitizing mutation negative non-squamous non-small cell lung cancer, CD274 low expression positive non-squamous non-small cell lung cancer, EGFR mutation-negative CD274 positive non-small cell lung cancer, or locally advanced non-squamous non-small cell lung cancer (Figure 1b,c). Canonical pathways regulation of the EMT by the growth factors pathway, the tumor microenvironment pathway, the nuclear factor erythroid 2-related factor 2 (NRF2)-mediated oxidative stress response, the regulation of the EMT in the development pathway, and the regulation of the EMT pathway were related to the carboplatin network (Figure 1b,c). Drugs were identified to have a direct relationship between the network, which included clazakizumab, and anti-interleukin (IL)-6 monoclonal antibody (Figure 1b,c). The IL-6 pathway was one of the components of signaling pathways in the regulation of the EMT by the growth factors pathway (Figure 1d). IL-6 activates the IL6 receptor (IL6R), leading to activation of Janus kinase (JAK), signal transducer and activator of transcription 3 (STAT3) and twist family bHLH transcription factor 1 (TWIST1), which subsequently induces cell migration, cell invasion, and metastasis (Figure 1d). Molecules in the regulation of the EMT by the growth factors pathway are listed in Table 2.

3.2. Causal Networks of Cisplatin-Treated Lung Adenocarcinoma

Causal networks of cisplatin treatment were investigated in IPA. In the IPA database, 169 analyses and 171 datasets were found to be related to cisplatin, among which 12 analyses were identified as having treatment of cisplatin (as of 2022) (Table 3). The numbers in the “Analysis Name” column in Table 3 indicate the ID name of the Ingenuity Pathway Analysis database (As of 2022). Causal network analysis of the cisplatin-treated samples revealed camptothecin as a master regulator of the causal network (Figure 2a). The causal network of camptothecin in cisplatin-treated lung adenocarcinoma included Fas cell surface death receptor (FAS) in the tumor microenvironment pathway, phosphatase and tensin homolog (PTEN) and MDM2 proto-oncogene (MDM2) in the cancer drug resistance by drug efflux and interferon regulatory factor 1 (IRF1) in the production of nitric oxide and reactive oxygen species in macrophages (Figure 2b). Upstream regulator analysis of cisplatin-treated lung adenocarcinoma revealed the upregulation in FAS, protein kinase C α (PRKCA), and cyclin-dependent kinase inhibitor 1A (CDKN1A) in the molecular mechanisms of cancer, and the involvement of the tumor microenvironment pathway in the causal network of cisplatin (Figure 3). CDKN1A, MDM2, and PTEN, which are involved in glioblastoma multiforme signaling, and PRKCA, which is involved in the production of nitric oxide and reactive oxygen species in macrophages, were also included in the cisplatin network (Figure 3).

3.3. Cisplatin as an Upstream Regulator of Gastric Adenocarcinoma

The upstream regulator analysis of cisplatin in the activity plot revealed 64,880 analyses for cisplatin (Figure 4a). Cisplatin was identified as an activated upstream regulator in pembrolizumab, a humanized IgG4 monoclonal antibody against programmed cell death-1 (PD-1)-treated gastric adenocarcinoma in progressive disease (Figure 4b) [43]. The mechanistic network of cisplatin included epidermal growth factor receptor (EGFR) and SMAD family member 3 (SMAD3) in the regulation of the EMT by the growth factors pathway and Akt, activator protein 1 (Ap1), early growth response 1 (EGR1), Fos proto-oncogene (FOS), Jun proto-oncogene (JUN), NFκB (complex), RELA proto-oncogene (RELA), and signal transducer and activator of transcription 3 (STAT3) in the regulation of the EMT by the growth factors pathway and the tumor microenvironment pathway (Figure 4b). miR-1195 (miRNAs w/seed GAGUUCG), mir-124, mir-338, mir-379, mir-434, mir-515, mir-622, MIR124, MIR499B, and CpG ODN/STAT3 siRNA CAS3/SS3 (CAS3/SS3) were identified as microRNAs and siRNA to have direct interactions with the network of cisplatin (Figure 4b, Table 4). Hypoxia-inducible factor 1 subunit α (HIF1A), STAT3, and RELA were the target molecules of the miRNAs. The tumor microenvironment pathway was activated in the pembrolizumab-treated gastric adenocarcinoma (Figure 4c). IL-6 and SPP1, which are regulated by M2 tumor-associated macrophages, were upregulated in the network (Figure 4c). Since the causal networks of camptothecin were activated in cisplatin-treated lung adenocarcinoma, the gene expression profiles of diffuse- and intestinal-type stomach adenocarcinomas were investigated in the causal network of camptothecin. Camptothecin was activated in intestinal-type stomach adenocarcinoma and inactivated in diffuse-type stomach adenocarcinoma (Figure 5).

3.4. Arsenic Trioxide Treatment in the EMT by Growth Factors Pathway

3.4.1. Regulatory Networks in Arsenic-Trioxide-Treated Liver Carcinoma

The network of arsenic trioxide was investigated in the EMT by the growth factors pathway, which identified regulatory networks of arsenic-trioxide-treated liver carcinoma (Table 5) [44]. The top regulatory network was involved in the cell death of carcinoma cell lines and cell proliferation of adenocarcinoma cell lines. Regulators of the network were amphiregulin (AREG), cytoskeleton-associated protein 2 like (CKAP2L), H2A.Z variant histone 1 (H2AZ1), HNF1A antisense RNA 1 (HNF1A-AS1), immunity-related GTPase family M member 1 (Irgm1), lin-9 DREAM MuvB core complex component (LIN9), MYB proto-oncogene like 2 (MYBL2), PCNA clamp associated factor (PCLAF), and S100 calcium binding protein A6 (S100A6). AREG is an epidermal growth factor receptor (EGFR) ligand located in extracellular space.

3.4.2. Causal Networks of Arsenic and Direct Interaction with microRNAs

A causal network of arsenic was activated in intestinal-type stomach adenocarcinoma and inactivated in diffuse-type stomach adenocarcinoma (Figure 6). The causal network of arsenic had direct RNA–RNA interactions with mir-101, mir-103, miR-125b-2-3pp (and other miRNAs w/seed CAAGUCA), mir-145, mir-22, mir-338, mir-379, mir-515, mir-622, and MIR499B (Table 6). mir-101 targets MTOR and mir-22 targets SP1, mir-103, mir-145, and mir125b-2-3pp (and other miRNAs w/seed CAAGUCA) target TP53, which are inhibited by arsenic (Figure 6). mir-370, mir-515, mir-338, MIR499B and mir-622 targets HIF1A, which is activated by arsenic (Figure 6).

3.5. Analysis of Oxaliplatin and SPINK1 Pancreatic Cancer Pathway

Network analysis of oxaliplatin revealed that oxaliplatin as a master regulator was predicted to be inhibited, and the serine peptidase inhibitor Kazal type 1 (SPINK1) pancreatic cancer pathway was inactivated in ischemic cardiomyopathy (Figure 7). EGFR involved in the SPINK1 general cancer pathway, regulation of the EMT by the growth factors pathway, the pulmonary fibrosis idiopathic signaling pathway, and hepatic fibrosis/hepatic stellate cell activation were decreased in the oxaliplatin-inhibitory network (Figure 7a). On the other hand, B cell leukemia/lymphoma 2 apoptosis regulator (BCL2), which is involved in the pulmonary fibrosis idiopathic signaling pathway, hepatic fibrosis/hepatic stellate cell activation, the coronavirus pathogenesis pathway, the tumor microenvironment pathway, and autophagy were increased in the oxaliplatin-inhibitory network (Figure 7a). The SPINK1 pancreatic cancer pathway consists of two main streams of signaling, which are the coagulation factor II (thrombin) receptor-like 1 (F2RL1)-transforming growth factor β receptor (TGFBR)-Smad2/3-metastasis axis and the serine protease (PRSS)–injury of pancreatic cells–acute recurrent pancreatitis–chronic pancreatitis axis (Figure 7b).

4. Discussion

Causal network analysis was used to investigate the molecular networks in treatment-resistant cancer, and the results revealed that EED226, a potent inhibitor of PRC2, is involved in DLBCL [42]. EED226 inhibits PRC2, which is resistant to an inhibitor of enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2), a subunit of PRC2 [42]. Since EED226 targets the trimethylated H3K27 binding pocket of EED, histone modification may be involved in cancer-treatment resistance.
Several miRNAs, MIR155, MIR192, MIR34A, and MIR200, are components of the regulation of the EMT by the growth factors pathway. mir-155_5p was upregulated at lung metastasis and peritoneal metastasis of colorectal cancer, compared to primary colorectal cancer [45]. Prior investigation of relationships between platinum drugs such as cisplatin and carboplatin and non-coding RNAs demonstrated that miRNAs are correlated with cisplatin activity [46]. Additionally, recent studies revealed the association between the EMT and miRNAs and their roles in drug resistance [24,47,48,49]. Downregulation of miRNA-214 induces the EMT and the migration and invasion of gastric cancer [50].
Pembrolizumab, a humanized IgG4 monoclonal antibody against PD-1, is an immune checkpoint inhibitor and can be used for microsatellite instability-high or tumor mutational burden-high advanced gastric cancer [51]. The overall response rate of pembrolizumab in advanced PD-L1-positive gastric cancer was 22.2% [52]. The patients in the IL-1R1high subgroup demonstrated a significantly lower response to pembrolizumab than those in the IL-1R1low subgroup [53]. Sundar et al. found a potential role of alternative promoter utilization as a predictive biomarker for resistance to immune checkpoint inhibition [54]. The interferon (IFN)-γ-related gene-expression profile predicted response to pembrolizumab [55]. Since the tumor microenvironment pathway was activated in pembrolizumab-treated gastric adenocarcinoma, the treatment resistance is involved in the tumor microenvironment pathway.
In the current study, HIF1A was identified as a target of several miRNAs in the causal network of arsenic. HIF1A and CDKN1A, together with miR-3607-3p, miR-301a-3p, and miR-93-5p, are associated with prolonged survival in glioblastoma patients treated with regorafenib [56]. miR-101 induces HIF1A-mediated apoptosis and cell cycle arrest [57]. HIF1A may be regulated by miRNAs in the causal network of arsenic. Since the activity state of the EMT pathway can be predicted in artificial-intelligence-based modeling, molecular network pathway analysis may be applicable to predict the molecular-induced responses [41].
F2RL1, F2R like trypsin receptor 1 or proteinase-activated receptor 2 (PAR2), was identified in the SPINK1 pancreatic cancer pathway. F2RL1 is a G protein-coupled receptor that is activated by distinct serine proteases to regulate receptor-specific signaling pathways involved in cell migration [58]. PAR2/PAR1 and TGF-β/ transforming growth factor β receptor 1 (TGFBR1 or ALK5) form a complex regulatory network in fibrosis and cancer [59]. PAR2, highly expressed in pancreatic ductal adenocarcinoma, is essential for TGF-β1-induced cell motility and TGFBR1 expression [60]. Future investigation is required to explore the RNA regulation of F2RL1 and the signaling pathways.
Regarding RNA regulation, miRNAs, including mir-124, mir-338, mir-379, mir-434, mir-515, and mir-622, were identified as interacting with the cisplatin network in gastric adenocarcinoma. mir-124 is implicated in regulating the sensitivity of renal cancer cells to cisplatin-induced necroptosis [61]. The sensitivity of lung cancer cells to cisplatin is changed by mir-124 [62]. These recent reports suggested that mir-124 oriented regulation of sensitivity of cancer toward cisplatin. The molecular networks were predicted to interact with several miRNAs, which may contribute to the identification of new drug targets for drug-resistant cancer.
The main mechanism of drug resistance is a DNA-platinum drug complex, which induces a DNA repair system [7]. The limitation of this study is the lack of elucidating the exact mechanism regarding the correlation between platinum complexes and the EMT. Platinum drug treatment could coactivate DNA repair mechanisms, cell cycles, and the EMT signaling pathways. It is also possible that some EMT regulation is involved during platinum drug treatment. Clarifying these possibilities would be a future challenge.
In this study, we investigated the altered molecular networks in metallodrugs, including platinum drugs and arsenic trioxide, and their relation to miRNAs, which will contribute to revealing the precise mechanism of drug resistance and possible combination therapy in clinical applications. The identification of target miRNAs in molecular networks would lead to the development of cancer therapy and strategies for precision medicines in practice.

5. Conclusions

Platinum drug treatment is related to the tumor microenvironment pathway in cancer. Cisplatin was identified as the upstream regulator of pembrolizumab-treated gastric adenocarcinoma. Several microRNAs were identified to interact with the cisplatin network. The carboplatin network was related to the regulation of the EMT pathway and the tumor microenvironment pathway. A close correlation between anti-cancer drug resistance and tumor microenvironment pathways needs to be revealed in future investigations.

Author Contributions

Conceptualization, S.T.; formal analysis, S.T.; investigation, S.T., E.B., T.H. and H.C.; writing—original draft preparation, S.T. and H.C.; writing—review and editing, S.T., S.Q., R.O., H.C., K.A., H.Y., E.J.P. and H.S.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Japan Agency for Medical Research and Development (AMED), grant numbers JP21mk0101216 (ST), JP22mk0101216 (ST) and JP23mk0101216 (ST), Japan Society for the Promotion of Science (JSPS) KAKENHI, grant number 21K12133 (ST and RO).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets analyzed for this study can be found in the Gene Expression Omnibus (GEO) GSE81267 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81267, accessed on 13 November 2023) and GSE6970 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6907, accessed on 13 November 2023), and The Cancer Genome Atlas (TCGA) of the cBioPortal for Cancer Genomics database at the National Cancer Institute (NCI) Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/, accessed on 13 November 2023).

Acknowledgments

The authors would like to acknowledge members of the National Institute of Health Sciences (NIHS), Japan. This research was supported by the Ministry of Health, Labour, and Welfare (MHLW), Japan.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Roman, D.A.; Pizarro, I.; Rivera, L.; Avila, J.; Cortes, P. Magnesium, zinc, arsenic, selenium and platinum urinary excretion from cancer patients of Antofagasta region, Chile: Multi-metal approach. JRSM Open 2016, 7, 2054270416660932. [Google Scholar] [CrossRef] [PubMed]
  2. Zhang, Y.; Pan, D.; Yang, H.; Huang, J.; He, Z.; Li, H.; Li, D. Effects of arsenic trioxide combined with platinum drugs in treatment of cervical cancer: A protocol for systematic review and meta-analysis of randomized controlled trials. Medicine 2020, 99, e22950. [Google Scholar] [CrossRef]
  3. Chen, H.; Pazicni, S.; Krett, N.L.; Ahn, R.W.; Penner-Hahn, J.E.; Rosen, S.T.; O’Halloran, T.V. Coencapsulation of arsenic- and platinum-based drugs for targeted cancer treatment. Angew. Chem. Int. Ed. Engl. 2009, 48, 9295–9299. [Google Scholar] [CrossRef] [PubMed]
  4. He, Z.Y.; Li, H.Y.; Yan, J.; Li, S.J.; Li, D.C.; Liang, Z.Z. A prospective trial to evaluate the clinical efficacy and safety of neoadjuvant chemotherapy with arsenic trioxide and carboplatin in locally advanced cervical cancer: A study protocol for randomized controlled clinical. Trials 2022, 23, 556. [Google Scholar] [CrossRef] [PubMed]
  5. Desoize, B. Metals and metal compounds in cancer treatment. Anticancer Res. 2004, 24, 1529–1544. [Google Scholar]
  6. Dasari, S.; Tchounwou, P.B. Cisplatin in cancer therapy: Molecular mechanisms of action. Eur. J. Pharmacol. 2014, 740, 364–378. [Google Scholar] [CrossRef]
  7. Makovec, T. Cisplatin and beyond: Molecular mechanisms of action and drug resistance development in cancer chemotherapy. Radiol. Oncol. 2019, 53, 148–158. [Google Scholar] [CrossRef]
  8. Hoang, D.H.; Buettner, R.; Valerio, M.; Ghoda, L.; Zhang, B.; Kuo, Y.H.; Rosen, S.T.; Burnett, J.; Marcucci, G.; Pullarkat, V.; et al. Arsenic Trioxide and Venetoclax Synergize against AML Progenitors by ROS Induction and Inhibition of Nrf2 Activation. Int. J. Mol. Sci. 2022, 23, 6568. [Google Scholar] [CrossRef]
  9. Cingam, S.R.; Koshy, N.V. Acute Promyelocytic Leukemia. In StatPearls; StatPearls Publishing LLC.: Treasure Island, FL, USA, 2022. [Google Scholar]
  10. Au, W.-Y.; Kwong, Y.-L. Arsenic trioxide: Safety issues and their management. Acta Pharmacol. Sin. 2008, 29, 296–304. [Google Scholar] [CrossRef]
  11. Paul, N.P.; Galván, A.E.; Yoshinaga-Sakurai, K.; Rosen, B.P.; Yoshinaga, M. Arsenic in medicine: Past, present and future. BioMetals 2023, 36, 283–301. [Google Scholar] [CrossRef]
  12. Lazo, G.; Kantarjian, H.; Estey, E.; Thomas, D.; O’Brien, S.; Cortes, J. Use of arsenic trioxide (As2O3) in the treatment of patients with acute promyelocytic leukemia: The M. D. Anderson experience. Cancer 2003, 97, 2218–2224. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, S.-H.; Chang, J.-Y. New Insights into Mechanisms of Cisplatin Resistance: From Tumor Cell to Microenvironment. Int. J. Mol. Sci. 2019, 20, 4136. [Google Scholar] [CrossRef] [PubMed]
  14. Lugones, Y.; Loren, P.; Salazar, L.A. Cisplatin Resistance: Genetic and Epigenetic Factors Involved. Biomolecules 2022, 12, 1365. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, L.; Zhao, X.; Fu, J.; Xu, W.; Yuan, J. The Role of Tumour Metabolism in Cisplatin Resistance. Front. Mol. Biosci. 2021, 8, 691795. [Google Scholar] [CrossRef]
  16. Jang, T.H.; Huang, W.C.; Tung, S.L.; Lin, S.C.; Chen, P.M.; Cho, C.Y.; Yang, Y.Y.; Yen, T.C.; Lo, G.H.; Chuang, S.E.; et al. MicroRNA-485-5p targets keratin 17 to regulate oral cancer stemness and chemoresistance via the integrin/FAK/Src/ERK/β-catenin pathway. J. Biomed. Sci. 2022, 29, 42. [Google Scholar] [CrossRef]
  17. Lin, X.; Wang, F.; Chen, J.; Liu, J.; Lin, Y.B.; Li, L.; Chen, C.B.; Xu, Q. N(6)-methyladenosine modification of CENPK mRNA by ZC3H13 promotes cervical cancer stemness and chemoresistance. Mil. Med. Res. 2022, 9, 19. [Google Scholar] [CrossRef]
  18. Zang, H.; Peng, J.; Wang, W.; Fan, S. Roles of microRNAs in the resistance to platinum based chemotherapy in the non-small cell lung cancer. J. Cancer 2017, 8, 3856–3861. [Google Scholar] [CrossRef]
  19. Zhang, S.; Deen, S.; Storr, S.J.; Chondrou, P.S.; Nicholls, H.; Yao, A.; Rungsakaolert, P.; Martin, S.G. Calpain system protein expression and activity in ovarian cancer. J. Cancer Res. Clin. Oncol. 2019, 145, 345–361. [Google Scholar] [CrossRef]
  20. Sui, Q.; Chen, Z.; Hu, Z.; Huang, Y.; Liang, J.; Bi, G.; Bian, Y.; Zhao, M.; Zhan, C.; Lin, Z.; et al. Cisplatin resistance-related multi-omics differences and the establishment of machine learning models. J. Transl. Med. 2022, 20, 171. [Google Scholar] [CrossRef]
  21. Li, P.; Song, R.; Yin, F.; Liu, M.; Liu, H.; Ma, S.; Jia, X.; Lu, X.; Zhong, Y.; Yu, L.; et al. circMRPS35 promotes malignant progression and cisplatin resistance in hepatocellular carcinoma. Mol. Ther. 2022, 30, 431–447. [Google Scholar] [CrossRef]
  22. Sheng, S.; Su, W.; Mao, D.; Li, C.; Hu, X.; Deng, W.; Yao, Y.; Ji, Y. MicroRNA-21 induces cisplatin resistance in head and neck squamous cell carcinoma. PLoS ONE 2022, 17, e0267017. [Google Scholar] [CrossRef] [PubMed]
  23. Tolue Ghasaban, F.; Maharati, A.; Zangouei, A.S.; Zangooie, A.; Moghbeli, M. MicroRNAs as the pivotal regulators of cisplatin resistance in head and neck cancers. Cancer Cell Int. 2023, 23, 170. [Google Scholar] [CrossRef] [PubMed]
  24. Pan, G.; Liu, Y.; Shang, L.; Zhou, F.; Yang, S. EMT-associated microRNAs and their roles in cancer stemness and drug resistance. Cancer Commun. 2021, 41, 199–217. [Google Scholar] [CrossRef] [PubMed]
  25. Mashouri, L.; Yousefi, H.; Aref, A.R.; Ahadi, A.M.; Molaei, F.; Alahari, S.K. Exosomes: Composition, biogenesis, and mechanisms in cancer metastasis and drug resistance. Mol. Cancer 2019, 18, 75. [Google Scholar] [CrossRef] [PubMed]
  26. Du, B.; Shim, J.S. Targeting Epithelial-Mesenchymal Transition (EMT) to Overcome Drug Resistance in Cancer. Molecules 2016, 21, 965. [Google Scholar] [CrossRef]
  27. Nilsson, M.B.; Sun, H.; Robichaux, J.; Pfeifer, M.; McDermott, U.; Travers, J.; Diao, L.; Xi, Y.; Tong, P.; Shen, L.; et al. A YAP/FOXM1 axis mediates EMT-associated EGFR inhibitor resistance and increased expression of spindle assembly checkpoint components. Sci. Transl. Med. 2020, 12, eaaz4589. [Google Scholar] [CrossRef]
  28. Jin, H.; He, Y.; Zhao, P.; Hu, Y.; Tao, J.; Chen, J.; Huang, Y. Targeting lipid metabolism to overcome EMT-associated drug resistance via integrin beta3/FAK pathway and tumor-associated macrophage repolarization using legumain-activatable delivery. Theranostics 2019, 9, 265–278. [Google Scholar] [CrossRef]
  29. Gaianigo, N.; Melisi, D.; Carbone, C. EMT and Treatment Resistance in Pancreatic Cancer. Cancers 2017, 9, 122. [Google Scholar] [CrossRef]
  30. Liu, X.; He, M.; Li, L.; Wang, X.; Han, S.; Zhao, J.; Dong, Y.; Ahmad, M.; Li, L.; Zhang, X.; et al. EMT and Cancer Cell Stemness Associated With Chemotherapeutic Resistance in Esophageal Cancer. Front. Oncol. 2021, 11, 672222. [Google Scholar] [CrossRef]
  31. Han, M.L.; Zhao, Y.F.; Tan, C.H.; Xiong, Y.J.; Wang, W.J.; Wu, F.; Fei, Y.; Wang, L.; Liang, Z.Q. Cathepsin L upregulation-induced EMT phenotype is associated with the acquisition of cisplatin or paclitaxel resistance in A549 cells. Acta Pharmacol. Sin. 2016, 37, 1606–1622. [Google Scholar] [CrossRef]
  32. Liang, F.; Ren, C.; Wang, J.; Wang, S.; Yang, L.; Han, X.; Chen, Y.; Tong, G.; Yang, G. The crosstalk between STAT3 and p53/RAS signaling controls cancer cell metastasis and cisplatin resistance via the Slug/MAPK/PI3K/AKT-mediated regulation of EMT and autophagy. Oncogenesis 2019, 8, 59. [Google Scholar] [CrossRef] [PubMed]
  33. Luo, J.; Yao, J.F.; Deng, X.F.; Zheng, X.D.; Jia, M.; Wang, Y.Q.; Huang, Y.; Zhu, J.H. 14, 15-EET induces breast cancer cell EMT and cisplatin resistance by up-regulating integrin alphavbeta3 and activating FAK/PI3K/AKT signaling. J. Exp. Clin. Cancer Res. 2018, 37, 23. [Google Scholar] [CrossRef] [PubMed]
  34. Bass, A.J.; Thorsson, V.; Shmulevich, I.; Reynolds, S.M.; Miller, M.; Bernard, B.; Hinoue, T.; Laird, P.W.; Curtis, C.; Shen, H.; et al. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014, 513, 202–209. [Google Scholar] [CrossRef]
  35. Cerami, E.; Gao, J.; Dogrusoz, U.; Gross, B.E.; Sumer, S.O.; Aksoy, B.A.; Jacobsen, A.; Byrne, C.J.; Heuer, M.L.; Larsson, E.; et al. The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discov. 2012, 2, 401–404. [Google Scholar] [CrossRef]
  36. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; et al. Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. 2013, 6, pl1. [Google Scholar] [CrossRef]
  37. Grossman, R.L.; Heath, A.P.; Ferretti, V.; Varmus, H.E.; Lowy, D.R.; Kibbe, W.A.; Staudt, L.M. Toward a Shared Vision for Cancer Genomic Data. N. Engl. J. Med. 2016, 375, 1109–1112. [Google Scholar] [CrossRef]
  38. Tanabe, S.; Quader, S.; Ono, R.; Cabral, H.; Aoyagi, K.; Hirose, A.; Yokozaki, H.; Sasaki, H. Molecular Network Profiling in Intestinal- and Diffuse-Type Gastric Cancer. Cancers 2020, 12, 3833. [Google Scholar] [CrossRef]
  39. Tanabe, S.; Quader, S.; Ono, R.; Cabral, H.; Aoyagi, K.; Hirose, A.; Yokozaki, H.; Sasaki, H. Cell Cycle Regulation and DNA Damage Response Networks in Diffuse- and Intestinal-Type Gastric Cancer. Cancers 2021, 13, 5786. [Google Scholar] [CrossRef]
  40. Krämer, A.; Green, J.; Pollard, J., Jr.; Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 2013, 30, 523–530. [Google Scholar] [CrossRef]
  41. Tanabe, S.; Quader, S.; Ono, R.; Cabral, H.; Aoyagi, K.; Hirose, A.; Perkins, E.J.; Yokozaki, H.; Sasaki, H. Regulation of Epithelial&ndash;Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction. Onco 2023, 3, 13–25. [Google Scholar] [CrossRef]
  42. Qi, W.; Zhao, K.; Gu, J.; Huang, Y.; Wang, Y.; Zhang, H.; Zhang, M.; Zhang, J.; Yu, Z.; Li, L.; et al. An allosteric PRC2 inhibitor targeting the H3K27me3 binding pocket of EED. Nat. Chem. Biol. 2017, 13, 381–388. [Google Scholar] [CrossRef] [PubMed]
  43. Kim, S.T.; Cristescu, R.; Bass, A.J.; Kim, K.M.; Odegaard, J.I.; Kim, K.; Liu, X.Q.; Sher, X.; Jung, H.; Lee, M.; et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat. Med. 2018, 24, 1449–1458. [Google Scholar] [CrossRef] [PubMed]
  44. Kawata, K.; Yokoo, H.; Shimazaki, R.; Okabe, S. Classification of heavy-metal toxicity by human DNA microarray analysis. Environ. Sci. Technol. 2007, 41, 3769–3774. [Google Scholar] [CrossRef] [PubMed]
  45. Høye, E.; Fromm, B.; Böttger, P.H.M.; Domanska, D.; Torgunrud, A.; Lund-Andersen, C.; Abrahamsen, T.W.; Fretland, Å.A.; Dagenborg, V.J.; Lorenz, S.; et al. A comprehensive framework for analysis of microRNA sequencing data in metastatic colorectal cancer. NAR Cancer 2022, 4, zcab051. [Google Scholar] [CrossRef] [PubMed]
  46. Biersack, B. Relations between approved platinum drugs and non-coding RNAs in mesothelioma. Noncoding RNA Res. 2018, 3, 161–173. [Google Scholar] [CrossRef]
  47. Petri, B.J.; Klinge, C.M. Regulation of breast cancer metastasis signaling by miRNAs. Cancer Metastasis Rev. 2020, 39, 837–886. [Google Scholar] [CrossRef]
  48. Khan, A.Q.; Ahmed, E.I.; Elareer, N.R.; Junejo, K.; Steinhoff, M.; Uddin, S. Role of miRNA-Regulated Cancer Stem Cells in the Pathogenesis of Human Malignancies. Cells 2019, 8, 840. [Google Scholar] [CrossRef]
  49. Wong, C.H.; Lou, U.K.; Fung, F.K.; Tong, J.H.M.; Zhang, C.H.; To, K.F.; Chan, S.L.; Chen, Y. CircRTN4 promotes pancreatic cancer progression through a novel CircRNA-miRNA-lncRNA pathway and stabilizing epithelial-mesenchymal transition protein. Mol. Cancer 2022, 21, 10. [Google Scholar] [CrossRef]
  50. Wang, R.; Sun, Y.; Yu, W.; Yan, Y.; Qiao, M.; Jiang, R.; Guan, W.; Wang, L. Downregulation of miRNA-214 in cancer-associated fibroblasts contributes to migration and invasion of gastric cancer cells through targeting FGF9 and inducing EMT. J. Exp. Clin. Cancer Res. 2019, 38, 20. [Google Scholar] [CrossRef]
  51. Takei, S.; Kawazoe, A.; Shitara, K. The New Era of Immunotherapy in Gastric Cancer. Cancers 2022, 14, 1054. [Google Scholar] [CrossRef]
  52. Chen, K.H.; Yuan, C.T.; Tseng, L.H.; Shun, C.T.; Yeh, K.H. Case report: Mismatch repair proficiency and microsatellite stability in gastric cancer may not predict programmed death-1 blockade resistance. J. Hematol. Oncol. 2016, 9, 29. [Google Scholar] [CrossRef] [PubMed]
  53. Zhang, P.; Gu, Y.; Fang, H.; Cao, Y.; Wang, J.; Liu, H.; Zhang, H.; Li, H.; He, H.; Li, R.; et al. Intratumoral IL-1R1 expression delineates a distinctive molecular subset with therapeutic resistance in patients with gastric cancer. J. Immunother. Cancer 2022, 10, e004047. [Google Scholar] [CrossRef] [PubMed]
  54. Sundar, R.; Huang, K.K.; Qamra, A.; Kim, K.M.; Kim, S.T.; Kang, W.K.; Tan, A.L.K.; Lee, J.; Tan, P. Epigenomic promoter alterations predict for benefit from immune checkpoint inhibition in metastatic gastric cancer. Ann. Oncol. 2019, 30, 424–430. [Google Scholar] [CrossRef] [PubMed]
  55. Ayers, M.; Lunceford, J.; Nebozhyn, M.; Murphy, E.; Loboda, A.; Kaufman, D.R.; Albright, A.; Cheng, J.D.; Kang, S.P.; Shankaran, V.; et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Investig. 2017, 127, 2930–2940. [Google Scholar] [CrossRef] [PubMed]
  56. Santangelo, A.; Rossato, M.; Lombardi, G.; Benfatto, S.; Lavezzari, D.; De Salvo, G.L.; Indraccolo, S.; Dechecchi, M.C.; Prandini, P.; Gambari, R.; et al. A molecular signature associated with prolonged survival in glioblastoma patients treated with regorafenib. Neuro Oncol. 2021, 23, 264–276. [Google Scholar] [CrossRef]
  57. Liu, N.; Xia, W.Y.; Liu, S.S.; Chen, H.Y.; Sun, L.; Liu, M.Y.; Li, L.F.; Lu, H.M.; Fu, Y.J.; Wang, P.; et al. MicroRNA-101 targets von Hippel-Lindau tumor suppressor (VHL) to induce HIF1alpha mediated apoptosis and cell cycle arrest in normoxia condition. Sci. Rep. 2016, 6, 20489. [Google Scholar] [CrossRef]
  58. McCoy, K.L.; Traynelis, S.F.; Hepler, J.R. PAR1 and PAR2 couple to overlapping and distinct sets of G proteins and linked signaling pathways to differentially regulate cell physiology. Mol. Pharmacol. 2010, 77, 1005–1015. [Google Scholar] [CrossRef]
  59. Ungefroren, H.; Gieseler, F.; Kaufmann, R.; Settmacher, U.; Lehnert, H.; Rauch, B.H. Signaling Crosstalk of TGF-β/ALK5 and PAR2/PAR1: A Complex Regulatory Network Controlling Fibrosis and Cancer. Int. J. Mol. Sci. 2018, 19, 1568. [Google Scholar] [CrossRef]
  60. Zeeh, F.; Witte, D.; Gädeken, T.; Rauch, B.H.; Grage-Griebenow, E.; Leinung, N.; Fromm, S.J.; Stölting, S.; Mihara, K.; Kaufmann, R.; et al. Proteinase-activated receptor 2 promotes TGF-β-dependent cell motility in pancreatic cancer cells by sustaining expression of the TGF-β type I receptor ALK5. Oncotarget 2016, 7, 41095–41109. [Google Scholar] [CrossRef]
  61. Mao, Q.; Zhuang, Q.; Shen, J.; Chen, Z.; Xue, D.; Ding, T.; He, X. MiRNA-124 regulates the sensitivity of renal cancer cells to cisplatin-induced necroptosis by targeting the CAPN4-CNOT3 axis. Transl. Androl. Urol. 2021, 10, 3669–3683. [Google Scholar] [CrossRef]
  62. Qi, M.M.; Ge, F.; Chen, X.J.; Tang, C.; Ma, J. MiR-124 changes the sensitivity of lung cancer cells to cisplatin through targeting STAT3. Eur. Rev. Med. Pharmacol. Sci. 2019, 23, 5242–5250. [Google Scholar] [CrossRef] [PubMed]
Genes 14 02073 g001aGenes 14 02073 g001b
Figure 1. Carboplatin molecular network in cancer. (a) Causal network of carboplatin in EED226-treated diffuse large B-cell lymphoma. Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network. (b) Carboplatin bioprofiler network in diffuse-type stomach adenocarcinoma. (c) Carboplatin bioprofiler network in intestinal-type stomach adenocarcinoma. (d) Regulation of the EMT by growth factors pathway colored with expected activation states. The red color indicates activation, while the green color indicates inactivation.
Figure 1. Carboplatin molecular network in cancer. (a) Causal network of carboplatin in EED226-treated diffuse large B-cell lymphoma. Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network. (b) Carboplatin bioprofiler network in diffuse-type stomach adenocarcinoma. (c) Carboplatin bioprofiler network in intestinal-type stomach adenocarcinoma. (d) Regulation of the EMT by growth factors pathway colored with expected activation states. The red color indicates activation, while the green color indicates inactivation.
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Figure 2. Causal networks in cisplatin treatment and involvement of tumor microenvironment pathway. (a) Camptothecin was identified as a master regulator in cisplatin-treated samples. The activity z-score of the top 50 master regulators is shown in the heat map. (b) Tumor microenvironment pathway-related Fas cell surface death receptor (FAS) was located in the plasma membrane in the network of camptothecin in cisplatin-treated lung adenocarcinoma. Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network.
Figure 2. Causal networks in cisplatin treatment and involvement of tumor microenvironment pathway. (a) Camptothecin was identified as a master regulator in cisplatin-treated samples. The activity z-score of the top 50 master regulators is shown in the heat map. (b) Tumor microenvironment pathway-related Fas cell surface death receptor (FAS) was located in the plasma membrane in the network of camptothecin in cisplatin-treated lung adenocarcinoma. Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network.
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Figure 3. Upstream regulator analysis of cisplatin revealed the upregulation in FAS, PRKCA, and CDKN1A, and involvement of tumor microenvironment pathway (lung adenocarcinoma (LUAD) [lung] cisplatin 9800). Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network.
Figure 3. Upstream regulator analysis of cisplatin revealed the upregulation in FAS, PRKCA, and CDKN1A, and involvement of tumor microenvironment pathway (lung adenocarcinoma (LUAD) [lung] cisplatin 9800). Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network.
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Figure 4. Cisplatin as an upstream regulator in gastric adenocarcinoma. (a) The upstream regulator analysis of cisplatin in the activity plot revealed 64,880 analyses for cisplatin. (b) Cisplatin was identified as an activated upstream regulator in pembrolizumab, a humanized IgG4 monoclonal antibody against programmed cell death-1 (PD-1)-treated gastric adenocarcinoma in progressive disease. (c) The tumor microenvironment pathway was activated in the pembrolizumab-treated gastric adenocarcinoma.
Figure 4. Cisplatin as an upstream regulator in gastric adenocarcinoma. (a) The upstream regulator analysis of cisplatin in the activity plot revealed 64,880 analyses for cisplatin. (b) Cisplatin was identified as an activated upstream regulator in pembrolizumab, a humanized IgG4 monoclonal antibody against programmed cell death-1 (PD-1)-treated gastric adenocarcinoma in progressive disease. (c) The tumor microenvironment pathway was activated in the pembrolizumab-treated gastric adenocarcinoma.
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Figure 5. Causal network of camptothecin in diffuse- and intestinal-type stomach adenocarcinomas. (a) The network of camptothecin in diffuse-type stomach adenocarcinoma. (b) The network of camptothecin in intestinal-type stomach adenocarcinoma.
Figure 5. Causal network of camptothecin in diffuse- and intestinal-type stomach adenocarcinomas. (a) The network of camptothecin in diffuse-type stomach adenocarcinoma. (b) The network of camptothecin in intestinal-type stomach adenocarcinoma.
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Figure 6. Causal network of arsenic in diffuse- and intestinal-type stomach adenocarcinomas. (a) The network of arsenic in diffuse-type stomach adenocarcinoma. (b) The network of arsenic in intestinal-type stomach adenocarcinoma.
Figure 6. Causal network of arsenic in diffuse- and intestinal-type stomach adenocarcinomas. (a) The network of arsenic in diffuse-type stomach adenocarcinoma. (b) The network of arsenic in intestinal-type stomach adenocarcinoma.
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Figure 7. Network analysis of oxaliplatin revealed that oxaliplatin as a master regulator was predicted to be inhibited and the SPINK1 pancreatic cancer pathway was inactivated in ischemic cardiomyopathy. (a) The network of oxaliplatin. Oxaliplatin was predicted to be inhibited as a master regulator in ischemic cardiomyopathy. (b) The SPINK1 pancreatic cancer pathway was inactivated in ischemic cardiomyopathy. Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network.
Figure 7. Network analysis of oxaliplatin revealed that oxaliplatin as a master regulator was predicted to be inhibited and the SPINK1 pancreatic cancer pathway was inactivated in ischemic cardiomyopathy. (a) The network of oxaliplatin. Oxaliplatin was predicted to be inhibited as a master regulator in ischemic cardiomyopathy. (b) The SPINK1 pancreatic cancer pathway was inactivated in ischemic cardiomyopathy. Gene identifiers marked with an asterisk (*) indicate that multiple identifiers in the dataset file map to a single gene in the Global Molecular Network.
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Table 1. Platinum drugs were searched with the term “platinum” in Ingenuity Pathway Analysis.
Table 1. Platinum drugs were searched with the term “platinum” in Ingenuity Pathway Analysis.
NumberSymbol
1platinum
2Pt2+
3carboplatin
4dicycloplatin
5enloplatin
6eptaplatin
7iproplatin
8nedaplatin
9oxaliplatin
10picoplatin
11satraplatin
12sebriplatin
13zeniplatin
14platinum agent
15platinum chemotherapy regimen
16platinum-based doublet chemotherapy
17E platinum
18platinum (II) chloride
19platinum agent/trastuzumab
20cisplatin
21platinum chemotherapy regimen/radiotherapy
22platinum-based doublet chemotherapy/taxane
23platinum chemotherapy regimen/vinorelbine
24platinum chemotherapy/taxoid derivative
25platinum-based doublet chemotherapy/vinorelbine
26platinum-based triplet chemotherapy
27adjuvant chemotherapy/platinum agent
28cetuximab/platinum chemotherapy regimen
29gemcitabine/platinum chemotherapy
30paclitaxel/platinum chemotherapy regimen
31platinum-based neoadjuvant chemotherapy
32bevacizumab/platinum chemotherapy/taxoid derivative
33platinum-norsperimidine complex Pt3NSpd2
34capecitabine/platinum chemotherapy regimen
35bevacizumab/gemcitabine/platinum chemotherapy regimen
36platinum chemotherapy regimen/thoracic radiotherapy
37nonplatinum-based doublet chemotherapy
38platinum acetylacetonate-titanium dioxide nanoparticles
39BP-Cx1-platinum complex BP-C1
40cetuximab/5-fluorouracil/platinum chemotherapy
41etoposide/platinum chemotherapy regimen
42ormaplatin
43Bamet-UD2
44NC-4016
45fluoropyrimidine/platinum-based triplet chemotherapy
46(diaminocyclohexane)(diacetato)(dichloro)platinum
47lobaplatin
48non-pemetrexed containing platinum chemotherapy regimen
49triplatin tetranitrate
50dacplatinum
51cisplatin/etoposide
52cisplatin/docetaxel
53cisplatin/paclitaxel
54carboplatin/paclitaxel
55cisplatin/epirubicin/5-fluorouracil
Table 2. Molecules involved in the EMT by the growth factors pathway (Ingenuity Pathway Analysis).
Table 2. Molecules involved in the EMT by the growth factors pathway (Ingenuity Pathway Analysis).
LocationSymbol
CytoplasmAKTMIR155
CDC42MIR192
DOCK10MIR34A
ERK1/2MKK3/6
FRS2-GRB2-SHP2p38MAPK
GAB1PI3K
GRB2PTPN11
Grb2-Shc1-SosRAF
IkkRas
JAKRHOA
JnkSHC1
MAP2K4/7SMAD2/3
MAP3K1SMURF1
MAP3K7SOS
MAPK1TAB1
MEKVIM
MEST
Extracellular SpaceEGFMMP2
FGFMMP9
FGF dimerPDGF
HGFTgfbeta
IL6TGFB2
MMP1
NucleusAP1MTOR
EGR1NFkappaB
ESRP2SMAD2/3/4
ETS1SMAD4
FOSSNAI1
FOXC2SNAI2
FOXO1STAT3
GSCTCF3
GSK3BTWIST1
HMGA2ZEB1
HSF1ZEB2
ID2
Plasma MembraneCDH1MET
CDH2OCLN
CLDN3Pdgfr
EGFRPDGFRA
ERBB2RAC1
FGFRTGFBR1
FRS2TGFBR2
IL6RTnfreceptor
OtherLATSPAR6
MIR200Tnf
N-CadherinYAP/TAZ
Table 3. Analyses of cisplatin treatment (Ingenuity Pathway Analysis).
Table 3. Analyses of cisplatin treatment (Ingenuity Pathway Analysis).
Analysis NameComparison ContrastUpregulated log2 CutoffProject NameOrganism
1- breast carcinoma [breast] cisplatin 7438Sampling Time => 10 to 11 h after treatment vs. NA0.344GSE28274human
1- lung adenocarcinoma (LUAD) [lung] cisplatin 9800Treatment => cisplatin vs. none0.1224GSE6410human
1- malignant pleural mesothelioma [mesothelium] cisplatin 6821Treatment: Treat Time [hours] => 24 -> cisplatin vs. none0.1633GSE22445human
1- ovarian cancer [ovary] cisplatin 1667Experiment Group => cisplatin 1 day recovery 2 weeks vs. monolayer culture0.6374GSE144232human
140- normal control [liver] cisplatin 2334Treat Time [days]: Treatment => 1 -> cisplatin vs. DMSO1.1422GSE57805rat
2- breast carcinoma [breast] cisplatin 7439Sampling Time => 8 to 9 h after treatment vs. NA0.0815GSE28274human
2- malignant pleural mesothelioma [mesothelium] cisplatin; piroxicam 6822Treatment: Treat Time [h] => 24 -> cisplatin; piroxicam vs. none0.539GSE22445human
24- normal control [liver] cisplatin 2200Treat Time [days]: Treatment => 0.67 -> cisplatin vs. DMSO1.0049GSE57805rat
256- normal control [liver] cisplatin 2324Treatment: Treat Time [days] => cisplatin -> 1 vs. 0.670.2943GSE57805rat
37- glioblastoma (GBM) [brain] cisplatin 4128Treatment => cisplatin vs. DMSO0.4672GSE97460human
4- malignant pleural mesothelioma [mesothelium] cisplatin 6824Treatment: Treat Time [hours] => 8 -> cisplatin vs. none0.1641GSE22445human
5- malignant pleural mesothelioma [mesothelium] cisplatin; piroxicam 6825Treatment: Treat Time [hours] => 8 -> cisplatin; piroxicam vs. none0.1488GSE22445human
Table 4. microRNAs and a siRNA interacting with the cisplatin network in gastric adenocarcinoma.
Table 4. microRNAs and a siRNA interacting with the cisplatin network in gastric adenocarcinoma.
SymbolFamily
CAS3/SS3biologic drug
miR-1195 (miRNAs w/seed GAGUUCG)mature microRNA
mir-124microRNA
mir-338microRNA
mir-379microRNA
mir-434microRNA
mir-515microRNA
mir-622microRNA
MIR124group
MIR499BmicroRNA
Table 5. Regulatory networks of arsenic-trioxide-treated liver carcinoma.
Table 5. Regulatory networks of arsenic-trioxide-treated liver carcinoma.
IDDiseases & FunctionsKnown Regulator-Disease/Function Relationship
1Cell death of carcinoma cell lines, cell proliferation of adenocarcinoma cell lines11% (2/18)
2Death of embryo, homologous recombination of DNA0% (0/18)
3Cell cycle progression, cell proliferation of tumor cell lines65% (13/20)
4Cell death of breast cancer cell lines, cell survival0% (0/6)
5DNA recombination, formation of γ H2AX nuclear focus, incidence of lymphoma0% (0/15)
6Chromosomal aberration, DNA damage, homologous recombination of DNA, T-cell non-Hodgkin lymphoma13% (1/8)
7Formation of γ H2AX nuclear focus13% (1/8)
8Chromosomal aberration, DNA damage, T-cell non-Hodgkin lymphoma11% (1/9)
9Death of embryo0% (0/5)
10Chromosomal aberration, DNA damage13% (1/8)
Table 6. microRNAs that have direct RNA–RNA interactions with a causal network of arsenic.
Table 6. microRNAs that have direct RNA–RNA interactions with a causal network of arsenic.
microRNAs
mir-101
mir-103
miR-125b-2-3p (and other miRNAs w/seed CAAGUCA)
mir-145
mir-22
mir-338
mir-379
mir-515
mir-622
MIR499B
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Tanabe, S.; Boonstra, E.; Hong, T.; Quader, S.; Ono, R.; Cabral, H.; Aoyagi, K.; Yokozaki, H.; Perkins, E.J.; Sasaki, H. Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer. Genes 2023, 14, 2073. https://doi.org/10.3390/genes14112073

AMA Style

Tanabe S, Boonstra E, Hong T, Quader S, Ono R, Cabral H, Aoyagi K, Yokozaki H, Perkins EJ, Sasaki H. Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer. Genes. 2023; 14(11):2073. https://doi.org/10.3390/genes14112073

Chicago/Turabian Style

Tanabe, Shihori, Eger Boonstra, Taehun Hong, Sabina Quader, Ryuichi Ono, Horacio Cabral, Kazuhiko Aoyagi, Hiroshi Yokozaki, Edward J. Perkins, and Hiroki Sasaki. 2023. "Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer" Genes 14, no. 11: 2073. https://doi.org/10.3390/genes14112073

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