Anticancer genes (NOXA, PAR-4, TRAIL) are de-regulated in breast cancer patients and can be targeted by using a ribosomal inactivating plant protein (riproximin)

Anticancer genes (NOXA, PAR-4, TRAIL) are de-regulated in breast cancer patients and can be targeted by using a ribosomal inactivating plant protein (riproximin) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Anticancer genes (NOXA, PAR-4, TRAIL) are de-regulated in breast cancer patients and can be targeted by using a ribosomal inactivating plant protein (riproximin) Asim Pervaiz, Nadia Naseem, Talha Saleem, Syed Mohsin Raza, Iqra Shaukat, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2466124/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2023 Read the published version in Molecular Biology Reports → Version 1 posted 5 You are reading this latest preprint version Abstract Background: Anticancer genes are endogenous enemies of transformed cells and impose antineoplastic effects upon ectopic expression. Identifying the expression profile of these genes is a prerequisite to explore their prognostic and therapeutic relevance in cancers. In parallel, natural compounds can be explored for their ability to upregulate anticancer genes in malignant cells for therapeutic purposes. In this study, we identified the expression levels of anticancer genes in breast cancer clinical isolates. In addition, the potential of a purified and sequenced plant protein (riproximin) to induce anticancer genes in breast cancer cells was evaluated. Methodology: Expression profiles of three anticancer genes (NOXA, PAR-4, TRAIL) were identified by immunohistochemistry in 45 breast cancer clinical isolates. Effects of riproximin exposure on expression of the anticancer genes were explored via microarray, real-time PCR and western blot methodologies. Lastly, the bioinformatic approach was adopted to highlight the molecular/functional significance of the anticancer genes. Results: NOXA expression was evenly de-regulated among the clinical isolates, while PAR-4 was significantly down-regulated in majority of the breast cancer tissues. In contrast, a higher TRAIL expression was observed in most of the clinical samples. Expression levels of the anticancer genes were following a distinct trend in accordance with the disease severity. Riproximin showed a substantial potential of inducing the anticancer genes in breast cancer cells at transcriptomic and protein levels. The bioinformatic approach revealed involvement of anticancer genes in multiple cellular functions and signaling cascades. Conclusion: Anticancer genes were de-regulated and showed discrete expression patterns in breast cancer patient samples. Riproximin effectively induced the expression of selected anticancer genes in breast cancer cells. Breast cancer Anticancer genes Riproximin Plant protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Anticancer genes (ACGs) are endogenous adversaries of cancer cells, which can induce antineoplastic effects when expressed ectopically. So far, a few genes (~ 10) have been assigned with the status of ACGs, while a systemic search for new members of this family is an on-going process [ 1 , 2 ]. Proteins translating from ACGs are complex in structures and interact specifically with their cellular counterparts to initiate cancer cell-specific death mechanisms. ACGs mediated antineoplastic effects include ER-stress, mitotic catastrophe, apoptosis and autophagy [ 3 – 5 ]. Currently, ACGs are being investigated actively for their potential role in cancer pathogenesis and therapeutic relevance. As per available data, low expression of ACGs is associated with poor prognosis, low survival rates, relapse of the disease conditions and resistance towards treatment modalities. Increased expression of ACGs is reported to sensitize cancer cells towards radio/chemotherapy and often induce synergistic effects in combination with the antineoplastic-agents [ 6 – 13 ]. As far as the therapeutic domain is concerned, most of the known ACGs are restricted to pre-clinical investigations till today. A predominant challenge is to find suitable delivery methods to have higher expression levels of these genes in cancer cells. In this context, ACG based immunotoxins, antibody conjugates and genetic engineering approaches are being tested [ 14 , 15 ]. Apart from these synthetic options, a highly neglected field is finding natural or synthetic compounds, which can induce the expression of ACGs in cancer cells. Identification of natural compounds with a safe physiological / biological profile is deemed crucial for exploiting ACGs “ the Achilles Heel of cancer cell ”. Ximenia americana is a plant that grows in tropical and subtropical areas of African and American countries. Kernels of this plant have long been used in African traditional medicine by local healers as a treatment for cancer [ 16 ]. Almost fifteen years ago, our group isolated and purified different protein fractions from aqueous extracts of X. americana kernels and tested their corresponding anticancer properties. A protein fraction characterized by size of ~ 60-kDa and substantial anticancer potential was identified and termed “ Riproximin ” [ 17 , 18 ]. DNA/peptide sequences showed that riproximin is a member of a big family consisting of ribosome-inactivating proteins (RIPs). RIPs are known for their catalytic nature and inhibit the translation procedure irreversibly in target cells by altering the 28S rRNA subunit [ 19 ]. As far as the molecular structure is concerned, riproximin is a heterodimer with two polypeptide chains (A- and B-chains) held together by an intermolecular disulphide bridge. The B-chain of riproximin has lectin-like properties and is responsible for binding to cell surface glycans, while the A-chain is accountable for subsequent catalytic activity [ 20 , 21 ]. Riproximin has demonstrated substantial antineoplastic effects against a variety of cancer cell lines including breast, colorectal, leukaemia, pancreatic and prostate, while sparing normal healthy cells [ 22 – 26 ]. In vivo studies are also documented where riproximin showed anticancer effects in colorectal and pancreatic cancer liver metastasis rat models [ 17 , 18 , 24 , 27 ]. Reported antineoplastic effects of riproximin include ER-stress, induction of unfolded protein response, cell cycle arrest, apoptosis and autophagy. In addition, riproximin halts various functional aspects of cancer cells including proliferation, migration and colony forming abilities [ 26 , 25 ]. In this study, we hypothesized that ACGs are differentially expressed in breast cancer and can be regulated via natural compounds for therapeutic purposes. To support this hypothesis, we identified the expressional levels of multiple ACGs (NOXA, PAR-4 and TRAIL) in 45 tumour samples of breast cancer via immunohistochemistry. Afterwards, the potential of riproximin to induce ACGs in two molecularly distinct breast cancer cell lines (MDA-MB-231 and MCF-7) was evaluated at transcriptome and proteome levels by using microarray, real-time PCR and western blot methodologies. Lastly, bioinformatic tools were applied to highlight the molecular/ functional importance of ACGs inside a cellular environment. Materials And Methods Clinical samples and histopathological analysis A total of 45 female patients, 18–55 years of age, presenting with primary breast cancers at the oncology units of Institute of Nuclear Medicine and Oncology (INMOL), Lahore, Services Hospital, Lahore and Allied Hospital, Faisalabad, Pakistan were included. After a complete radiological and histological diagnosis of malignancy on true-cut biopsy, patients were recruited for the study after obtaining their written informed consent. Patients with recurrence or on follow-up of malignancy were excluded. Modified radical mastectomy specimens were fixed in 10% neutral buffered formalin and grossly examined according to the College of American Pathologist (CAP) protocol. Paraffin embedded tissue sections were prepared and stained with H&E stain for confirmation of the histological diagnosis and grading was carried out following the Elston-Ellis modification of the Scarff - Bloom - Richardson grading system [ 28 ]. Immunohistochemistry Of Noxa, Par-4 And Trail About 4–6µm sections were taken on positively charged albumin coated slides for immunohistochemistry. The slides were placed in a hot air oven at 70°C for 30 minutes, followed by three washing steps in xylene, for 1 minute each. Paraffin removal was continued by three washing steps in alcohol, 1 minute each. Then, the sections were treated with H 2 O 2 for 5 minutes to inhibit endogenous peroxidase and rehydrated by washing them in H 2 O. Epitope retrieval (unmasking of antigen) was done by using ethylenediaminetetraacetic acid (EDTA) buffer for 50 minutes at pH8 and 90°C. Slides were held at room temperature for 20 minutes and then washed in 1x PBS for 10 minutes. A blocking reagent was applied for 10 minutes and slides were again washed with PBS for 1 minute. Sections were incubated for 30 minutes with the primary antibodies for ER, PR, HER2neu, NOXA, PAR-4 and TRAIL antibodies. After washing in PBS, sections were incubated for 20 minutes with the secondary antibody and again washed with PBS. DAB was used as chromogen for 5–8 minutes and again the slides were washed with PBS for 5 minutes. For counter staining, slides were dipped in haematoxylin and cleaned in xylene for 5 minutes. Finally, slides were mounted with DPX and examined under a microscope. For NOXA (cat#PA5-19977, Invitrogen), positive staining was localized to the tumor cell cytoplasm and was scored based on intensity (0/1/2/3) and percent tumor cell positivity [grouped into quartiles (0–4)]. Intensity and immunopercent were multiplied to yield a final immune-score. For purposes of analysis, NOXA staining was dichotomized as negative/low versus elevated, where low was expanded to an immune-score of ≤ 4 while a score > 4 was considered as an elevated expression [ 29 ]. For PAR-4 staining (cat#PA5-77686, Invitrogen), immunohistochemical staining was assessed semi-quantitatively by multiplying both the intensity score (0/1/2/3) and the proportion of positive tumour cells (0, 0%; 1, 0–10%; 2, 10–50%; 3, 50–100%), (maximum possible, 9). For the statistical analysis, the weighted scores were grouped into two categories where scores of 0–3 were considered negative and 4–9 positive. For TRAIL immunohistochemistry (cat#PA5-102584, Invitrogen), a binominal category was adopted. The absence of TRAIL expression was defined as the absence of cytoplasmic staining in less than 10% of tumour cells while the presence of staining in at least 10% of tumour cells was considered as evidence of TRAIL expression [ 30 ]. For ER (cat#PA0151, Leica Biosystem) and PR (cat#PA0312, Leica Biosystem), Tumour Allred scoring scheme was adopted according to previously reported criteria [ 31 ]. A cut-off to define receptor positivity for ER and PR was an Allred score ⩾3, the internationally accepted cut-off. High scores were defined as Allred 6–8, and low scores as Allred 3–5. Regarding Her2 (cat#PA0983, Leica Biosystem) membrane staining, a previously published scoring scheme was adopted [ 32 ]. Cell Culture And Riproximin Two human breast cancer cell lines (MDA-MB-231 and MCF-7), obtained from American Type Culture Collection (ATCC, USA), were cultured in RPMI-1640 medium supplemented with L-glutamine (2 mM), foetal bovine serum (10%), streptomycin (100µg/ml) and penicillin (100IU/ml) under standard cell culture incubation conditions (5% CO2, 37˚C, humidified atmosphere). Cell lines were periodically checked (every 3 months) for mycoplasma contamination using VenorGem PCR kit (cat#11-1025, Minerva Biolabs) and passaged two to three times per week to maintain logarithmically growing cell populations. Riproximin was extracted and purified from the plant “ X. americana ”, dissolved in 1x PBS and stored at -25 0 C as described previously [ 23 ]. Real-time Pcr Analysis Breast cancer cell lines (MDA-MB-231 and MCF-7) were exposed to riproximin and expression modulations at transcriptome level were identified by qRT-PCR methodology for three ACGs (NOXA, PAR-4, TRAIL). To that purpose, the cell lines were cultured in 6-well culture plates (1.5×10 5 cells/well/2ml medium) and exposed for 48 hours to different inhibitory concentrations of riproximin (IC 25 , IC 50 , IC 75 ) selected based on our previous data [ 26 ]. Following the exposure period, cells were harvested, cell pellets collected, total RNA extracted by a commercial kit (cat#K0731, Thermo-Fisher Scientific) and cDNA was synthesized (cat#K1622, Thermo-Fisher Scientific). Prepared samples were subjected to transcript detection of the ACGs by using a mixture of gene specific primers (NOXA: ATTACCGCTGGCCTACTGTG, CAATGTGCTGAGTTGGCACT, PAR-4: GCATGCACACTAAAAACCAAAA, TGTGTCCCAGTGTTATTCTTCAA, TRAIL: ACGACAAACAAATGGTCCAA, AGCTCAAATATTCCCCCTTGA) and SybrGreen/ROX master mix (cat#K0221, Thermo-Fisher Scientific) in a QuantStudio 3 real-time PCR system. All samples were processed in triplicate while expression levels of untreated cells were used as controls. Expression of a reference gene (HPRT1) was used to normalize the data, whereas fold changes were identified by the 2-△△Ct method. Microarray Analysis Microarray analysis was performed to determine the modifications in gene expression with minor modifications to our previously published protocol [ 22 ]. Briefly, MDA-MB-231 cells were exposed to riproximin (IC 25 , IC 50 , IC 75 ) for 24, 48 and 72 hours followed by RNA extraction with a RNeasy Mini kit (cat#74004, Qiagen). The quality of the extracted RNA was determined by using total RNA Nano chip assay on an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA samples with sufficient RNA Integrity Number values (≥ 8.8) were selected for the expression profiling. Western Blot Analysis Effects of riproximin exposure on protein levels of ACGs (NOXA, PAR-4, TRAIL) in breast cancer cell lines were identified via western blot methodology. Briefly, the cells were cultured in 25cm 2 flasks (1.0×10 6 cells/flask/5ml medium) and exposed to different concentrations of riproximin (IC 25 , IC 50 ) for 48 hours. Following the exposure period, cells pellets were collected and lysed with RIPA lysis buffer. Extracted proteins were quantified via Bradford assay and a total of 30µg protein/sample was subjected to electrophoresis on 4–12% gradient polyacrylamide SDS gels. Afterwards, proteins were transferred onto nitrocellulose membrane and incubated for 2 hours at room temperature with specific primary antibodies for NOXA, PAR-4 or TRAIL (same as used for immunohistochemistry). Later on, membranes were rinsed with TBST buffer followed by incubation with alkaline-phosphatase (AP) conjugated secondary antibody for 1 hour at room temperature. Immunoreactive proteins were visualized with BCIP/NBT tablets (cat# B5655, Sigma), bands were analysed by using ImageJ software and levels of β-actin were used to normalize the protein expression data sets. Bioinformatics Analysis The protein-protein interaction (PPI) network was created by importing the physical subnetwork of target genes from STRING via StringApp plugin into Cytoscape version 3.9.1. The maximum of 100 interactors with a minimum interaction score of 0.15 were imported to construct the network. Cytoscape plugin NetworkAnalyzer version 4.4.8 was used to assess the topological parameters. PPI network clusters was detected by Markov Cluster Algorithm (MCL) clustering using Clustermaker2 app version 2.2 in Cytoscape. Clustering was performed with inflation value of 3.0 and stringdb score as array source. The genes’ list of the PPI network was imported into R and their corresponding ENTREZ IDs were fetched using “biomaRt” package version 2.52.0. Gene Ontology (GO) categories (Biological Process - BP, Molecular Function - MF and Cellular Component - CC) and KEGG pathways based functional enrichment was performed by “ClusterProfiler” package version 4.4.4 with the q-value cut off < 0.01. The three GO categories were visualized by “enrichplot” package version 1.16.2, while KEGG based enriched biological pathways were visualized by “GOplot” package version 1.0.2. Results Clinical diagnosis and molecular sub-types of breast cancer patients In the current study, a total of 45 cases of adult female patients with breast lumps having complete radiological evaluation at the time of diagnosis were included. The age of these patients ranged from 23 to 52 years; the mean age was 44.1 ± 10.03 years. Taking age groups into consideration, n = 20 (45.3%) patients were < 40 years of age and n = 25 (55.5%) patients were ≥ 40 years of age. The size of the tumor ranged from 1.8 to 21.5 cm with a mean value of 13.06 ± 4.2 cm. Regarding the histological type, invasive ductal carcinoma (IDC) was diagnosed in all 45 cases. The Nottingham scoring and grading system showed n = 8 (17.8%) cases in grade 1, n = 16 (35.6%) cases in grade 2 and n = 21 (46.6%) cases in grade 3. Clinical staging of these breast cancer cases was done according to American Joint Committee on Cancer (AJCC). There was n = 7 cases (15.5%) in stage I, n = 15 cases (33.3%) in stage II, n = 18 cases (40%) in stage III and 5 cases (11.1%) in stage IV of breast carcinoma. For the purpose of analysis, the patients were subdivided into early (stage I/II) and advanced (III/IV) stage groups as 22 (48.8%) and 23 (51.2%), respectively (Table 2 A-B). The molecular subtyping of breast cancer in increasing order of frequency were Her-2 type (HR-/HER2+), luminal B (HR+/HER2+), luminal A (HR+/HER2-) and basal like (HR-/HER2-), respectively (Table 1 A). Differential Expression Of Anticancer Genes In Breast Cancer Tissues Expression frequency of NOXA, PAR-4 and TRAIL with respect to all samples included (Table 1 B) and molecular subtypes of breast cancer in the current study (Table 1 C) was analysed in order to observe their statistical association. Histopathological expression levels of NOXA, PAR-4 and TRAIL genes were identified by immunohistochemistry. Examples of low or high expression are shown in Fig. 1 . The results shown in Table 1 C depict that higher expression of TRAIL and lower expression of PAR-4 correlated significantly (p < 0.05) with the luminal A and basal type of breast carcinomas, thus reflecting poor response to treatment and an unfavourable outcome. Expression of NOXA did not show any significant frequency distribution or correlation among the molecular subtypes of breast cancer. The cases of breast carcinoma with grade 1 showed higher frequency of TRAIL negative and NOXA elevated expression with no significant association. Grade 2 carcinomas showed a higher frequency of TRAIL positive, NOXA low/negative and PAR-4 negative expression with no significant association. Grade 3 cases demonstrated significant association with higher TRAIL expression and negative PAR-4 expression (Table 2 A). When the clinical stage groups were compared with the expression of TRAIL, NOXA and PAR-4, only loss of PAR4 expression showed significant association with the advanced stage group while higher TRAIL expression was seen in the advanced tumour stage group without statistical significance. In contrast, NOXA neither showed any significant frequency distribution nor statistical association with the clinical stage groups (Table 2 B). Time And Concentration Dependant Effects Of Riproximin Exposure On Anticancer Genes Evaluation of the time and concentration dependant effects of antineoplastic agents is an important piece of information from clinical perspective. With this notion, we evaluated the impact of various concentrations (IC 25 , IC 50 , IC 75 ) of riproximin on ACGs during three different exposure periods (24, 48, 72 hours) in MDA-MB-231 cells via microarray strategy. The analysis showed concentration dependent induction of NOXA and PAR-4 genes during early exposure periods (24 and 48 hours), while this trend diluted overtime (72 hours period) with minimal alterations in the two genes when compared with untreated controls. TRAIL gene was consistently inhibited to moderate levels (< 2fold) in the cells during the whole experimental period as shown in Fig. 2 . Riproximin Mediated Induction Of Transcript/protein Of Anticancer Genes Riproximin is known for inducing anticancer effects and altering the expression of multiple genes in breast cancer cells [ 26 ]. In this study, we particularly evaluated the impact of purified riproximin exposure on expressional changes at transcriptome and protein levels of the ACGs in two breast cancer cell lines. To that purpose, cell lines were exposed to riproximin (IC 25 -IC 75 ) followed by qRT-PCR and western blot for expression analysis. At the transcriptome level, ACGs were upregulated by enlarge in the two cell lines with a distinct response towards riproximin treatment (Fig. 3 A). Maximum induction in MDA-MB-231 cells was observed for NOXA (3.9fold), followed by PAR-4 (2.8fold) and TRAIL (1.5fold), respectively. In MCF-7 cells, TRAIL was the most up-regulated gene (9fold) followed by PAR-4 (8.9fold) and NOXA (3.4fold). Interestingly, maximum induction in the ACGs was observed in MDA-MB-231 cells when the highest concentration (IC 75 ) of riproximin was applied. Opposite to this phenomenon, maximum induction of ACG transcripts was observed in MCF-7 cells with lower concentrations (IC 25 , IC 50 ) of riproximin. As far as protein levels are concerned, a reasonable induction of NOXA was observed in MDA-MB-231 cells, while the other two ACGs (PAR-4 and TRAIL) were inhibited moderately. In MCF-7 cells, all the three ACGs were induced at protein levels at higher concentrations (IC 50 ) of riproximin. By enlarge, expressional modifications in NOXA and TRAIL genes followed the similar pattern at transcriptome and proteome levels in the two cell lines following riproximin exposure. In contrast, PAR-4 gene was substantially induced at transcriptome levels, while inhibited (MDA-MB-231) or moderately up-regulated (MCF-7) at protein levels (Fig. 3 B). This, in turn indicates the presence of a potential negative feed-back mechanism creating hinderance in translation of PAR-4 transcripts or requirement of longer intervals to show proportional expression of this gene at protein levels. Over all, riproximin showed a considerable potential for up-regulating the expression of multiple ACGs in breast cancer cell-specific manner. Effects Of Anticancer Genes On Multiple Molecular And Functional Arms Physical interactions-based PPI network of each target gene was imported from STRING into Cytoscape separately and then merged to get the maximum number of interactors imported. The final merged network had 96 nodes with 761 edges having median degree of 15.84. MCL based clustering revealed 5 gene clusters, with the biggest cluster having 33 nodes with 171 edges while the smallest with 3 nodes and 2 edges. Interestingly, all the target genes were having independent clusters while two cluster arising from this network were devoid of target genes. It was also interesting to note that both DIABLO and TP53 shared interactions with NOXA and TRAIL based clusters as shown in the Fig. 4 A. Furthermore, the PPI network included 11 enzymes (ARF3, CASP10, CASP3, CASP6, CASP8, F2, HUWE1, PPP2CA, RAB2A, UCHL1 and ZDHHC17) 7 transcription factors (FOXO3, GFI1, GFI1B, KLF6, MYC, MYCN and TP53) and 3 kinases (PAK1, RIPK1 and BTK) where all the transcription factors and majority of the enzymes were included in the 3rd cluster. Among 38 interactors of TRAIL, 8 were significantly enriched in “TNFR/NGFR cysteine-rich region” protein families (pfam) domain (PF00020) while 8 were enriched in the “Death domain” pfam domain (PF00531). Furthermore, among 29 interactors of NOXA, 8 were enriched in the “Apoptosis regulator proteins, Bcl-2 family” pfam domain (PF00452). On the contrary, among the 27 interactors of PAR-4, 12 were enriched in “GGL domain” pfam domain (PF00631), 7 enriched in “G-protein alpha subunit” pfam domain (PF00503) and 7 in the “ADP-ribosylation factor family” pfam domain (PF00025). In GO enrichment analysis significant enrichment of 440 biological processes (BP), 28 molecular functions (MF), 14 cellular components (CC), and 71 KEGG pathways was revealed (Fig. 4 B-E). Interestingly, the majority of BP enriched were related to extrinsic and intrinsic apoptosis signalling pathways and their regulation (GO:0097191, GO:0097193 & GO:2001233). In the MF category major enriched terms were GTPase activity (GO:0003924) and G-protein beta-subunit binding (GO:0031681) with the of 21 and 13 genes, respectively. For the category of cell component (CC), PPI network was mainly enriched in heterotrimeric G-protein complex (GO:0005834), GTPase complex (GO:1905360) and the extrinsic component of cytoplasmic side of the plasma membrane (GO:0031234). KEGG pathway enrichment analysis revealed that beside being enriched in viral infections and pathways of cancer, the PPI network genes were enriched in a lot of key pathways such as PI3K-Akt signalling pathway, Apoptosis, Chemokine signalling pathway, Ras signalling pathway, TNF signalling pathway and Relaxin signalling pathway. Discussion Precise targeting of the molecular factors associated with cancer pathogenesis is prerequisite for an effective cure. For this purpose, identification of novel therapeutic targets and development of corresponding molecules is a continuous process. ACGs are endogenous enemies of transformed cells and are being investigated for their prognostic and therapeutic relevance. Expressional profiling of various ACGs in cancers, their association with disease progression and involvement in treatment response is being studied by the scientific community. In parallel, novel delivery methods including antibody-based conjugates, immunotoxins development and genetic engineering strategies are being established to achieve ACGs mediated cancer cell-specific death. In addition to these synthetic strategies, naturally occurring compounds are also being considered for their potential to induce ACGs in cancer cells and to exploit resulting antitumor effects. In this study, as a first step, we identified the protein expression levels of three ACGs (NOXA, PAR-4, TRAIL) in breast cancer tissues by immunohistochemistry techniques (Fig. 1 ). To the best of our knowledge, this is the first study to investigate the expression profile of multiple ACGs at a time in breast cancer patients. At this time, we focused on IDC samples only, which is the most prevalent type of breast cancer. As far as the patient cohort is concerned, early onset of the disease (average ± 44 years age) with a considerable proportion of young patients (n = 20/45: <40 years age) was noticed, which is mainly attributed to the lack of awareness programs and late diagnosis locally. Molecular subtyping revealed a maximum number of cases as triple negative (Basal like) followed by Luminal A type, which itself predicts poor prognosis of these patients. As far as immunohistochemistry results are concerned, distinct ACG expression profiles were identified in breast cancer patient samples. An almost even distribution of NOXA expression (Neg/Low 21/45 = 47%, Elevated 24/45 = 53%) was found with no correlation with the molecular subtypes of breast cancer. However, a clear trend was found between NOXA expression and tumor grade, where Neg/Low expression ratios increased with increasing grade of the disease (Table 2 A). This, in turn, reflects continuous inhibition of NOXA expression overtime in patients with advanced stages of breast cancer. Similar findings have also been reported where low expression of NOXA was found associated with poor prognosis, reduced overall survival and chemo-resistance in breast cancer [ 6 , 7 ]. In our selected cohort, low expression of the PAR-4 gene was a dominant fact where 76% (34/45) of the samples were found negative for this ACG. Furthermore, a continuous decrease in PAR-4 expression was witnessed with the increasing severity of the disease. Precisely, 4/8 (50%), 12/16 (75%) and 18/21 (86%) cases of grade 1, 2 and 3 were found negative for PAR-4 expression, respectively (Table 2 A). The data is in line with other available reports where low expression of PAR-4 and its association with poor prognosis is reported in breast cancer [ 9 , 8 ]. Lower expression of PAR-4 was also correlated significantly (p < 0.05) with the luminal A and basal type of breast carcinomas, thus depicting the poor response to treatment and an unfavourable outcome. Similar findings have been reported, where PAR-4 expression was found to be low in the highly aggressive, estrogen-receptor negative (ER-), basal-like, and high-grade (grade 3) breast cancers, which are all associated with poor clinical outcome [ 33 ]. Alvarez and colleagues found lower PAR-4 expression associated with the poor response towards neoadjuvant chemotherapy and an increased risk of relapse in patients with breast cancer. Concurrently, higher expression of PAR-4 is also reported to sensitize triple-negative breast cancer cell lines to DNA damage-induced cell death by making cells more prone to chemo-sensitivity [ 34 ]. All in all, our data and available scientific reports suggest overall low expression of PAR-4 in breast cancer patients with a substantial association with aggressive nature, chemo-resistance and relapse of the disease. TRAIL is one of the most studied ACG till today and is being targeted in the field of cancer therapeutics. A major hurdle is to overcome the development of TRAIL resistance achieved by tumour cells. Despite these existing challenges, the development of specific molecules (agonists) against the TRAIL receptors and recombinant TRAIL is an on-going process. As far as the expression profile is concerned, TRAIL was the only gene where we found positive expression (at least 10% of tumour cells positively stained) in most of the clinical samples (32/45 = 71%, Table 1 B). This positivity of the expression was increased with the severity of the disease as shown by 3/8 (38%), 11/16 (69%) and 16/21 (76%) cases in grade 1, 2 and 3, respectively (Table 2 A). Higher levels of TRAIL correlated significantly (p < 0.05) with the luminal A and basal type of breast carcinomas. Grade 3 cases in the present study demonstrated a significant association with higher TRAIL expression and negative PAR-4 expression thus depicting that these two markers relate significantly with tumour aggressiveness and poor prognosis in breast cancer patients (Table 2 A). Over and above to these facts, astonishing findings are being reported where combination of TRAIL along with platinum drugs (cisplatin) and liposomal formulations have shown to be effective against cancer stem cells and circulating breast cancer cells, which in turn, gives us hope that exploiting ACGs can be effective in controlling secondary tumour development and recurrence of the disease [ 12 , 35 ]. Several natural compounds have shown the potential to up-regulate ACGs in cancer cells followed by induction of multidirectional antineoplastic effects [ 36 – 40 ]. As we know, by enlarge naturally occurring agents bear a safe physiological profile, thus exploiting such compounds to up-regulate ACGs in cancer cells seems to be an attractive research domain. Among the plant-based anticancer agents, RIPs comprise an important class of toxins with the promising capacity to be developed as therapeutic entities [ 41 ]. The potential effects of RIPs’ exposure on endogenous enemies of cancer cells (ACGs) are a neglected field so far. In a previous study, we reported that riproximin can induce significantly (˃100fold) the expression of a well-known ACG (MDA-7/IL24) in breast cancer cells [ 26 ]. In this particular study, we were interested to figure out the potential impact of riproximin on expressional profile of three further ACGs (NOXA, PAR-4, TRAIL). For this purpose, two breast cancer cell lines with distinct molecular subtypes (MDA-MB-231: triple negative, MCF-7: ER/PR positive) were selected and exposed to various concentrations of riproximin. Transcriptome and proteomic data revealed that riproximin can up-regulate the three ACGs in breast cancer cells (Fig. 3 ). Comparatively, more prominent induction of the three ACGs was observed in MCF-7 (up to 3.3fold NOXA, 8.9fold PAR-4, 9fold TRAIL) as compared to MDA-MB-231 cells (up to 3.8fold NOXA, 2.8fold PAR-4, 1.5fold TRAIL). In a previous study, we reported that riproximin can induce significant cytotoxic effects in MCF-7 cells after 48 hours of exposure at much lower concentrations (IC50:0.38ng/ml) as compared to MDA-MB-231 (IC50:3.6ng/ml) cells [ 26 ]. Based on previous data and current findings, it can be concluded that breast cancer cells with particular molecular sub-type (e.g., ER/PR positive status) are more sensitive towards riproximin exposure. One potential explanation could be that riproximin interacts with the cells through its lectin binding domains (B-chain) preferably to cell surface glycans (NA2/NA3) and N-acetyl-D-galactosamine (GalNAc/Tn Antigen) [ 20 ]. Thus, the presence and saturation status of specific glyco-targets along with the potential involvement of other cell surface receptors can modify the subsequent binding capacity and internalization of riproximin. Other than this, the possibility of wide-ranged downstream signalling cascades being targeted by riproximin in a cell-specific manner cannot be ruled out. As far as riproximin mediated induction of ACGs is concerned, the phenomenon is not restricted to breast cancer cells only, as we recently witnessed that this plant protein can induce the ACGs in multiple colorectal cancer cell lines [ 42 ]. To summarize, riproximin mediated up-regulation of ACGs can be exploited against various malignancies provided with further detailed pre-clinical investigations. Bioinformatics is an emerging interdisciplinary field being exploited in modern day biology to understand complex cellular mechanisms including roles of potential genes/proteins within a cell and extrapolation of the wet-lab results. With this notion, we were interested to highlight the molecular significance of ACGs in cell environment by using various in silico approaches. In the PPI based clustering built with the help of STRING and Cytoscape software, the ACGs showed independent clusters, which in turn, reflects their minimal interdependency on each other (Fig. 4 A). If so, this fact is of particular importance as it shows up-regulation of the ACGs via riproximin can initiate distinct and independent cascades to induce antineoplastic effects via multiple molecular directions. TP53 (a master regulator of apoptosis) and DIABLO (a proapoptotic mitochondrial protein) were the only two genes connected between NOXA and TRAIL clusters. The substantial involvement of NOXA and TRAIL in apoptotic routes is a known fact, thus linking bridges between these two ACGs via TP53 and DIABLO is not a surprising observation. PPI networks of the ACGs also showed presence of key enzymes, transcription factors and kinases in the clusters, which means ACGs are involved in mechanistic routes responsible for enzymatic activities, gene expression regulations and phosphorylation steps, hence bear a wide-ranged responsibility inside cells. GO enrichment analysis revealed a huge number of BP (440), MF (28), CC (14) and KEGG pathways (71) being associated with the ACGs (Fig. 4 B-E). Mainly these CC, MF and BP include GTPase complex formation, their corresponding activities and apoptotic signalling cascades, respectively. KEGG pathways data was also very enriched where ACGs were shown to be involved in vital mechanisms including survival (PI3K/Akt), apoptosis/necroptosis (caspases and BCL family), chemokine networking, TNF and RAS/RAF signalling cascades. All in all, the bioinformatic approach showed that the ACGs are extremely crucial factors involved in multidirectional aspects of a cell life and need due attention to explore their corresponding prognostic/therapeutic relevance in cancers. Overall, the findings of this study reflected a distinct expression pattern of ACGs in breast cancer clinical isolates. Targeting these endogenous enemies of transformed cells can be instrumental in the field of cancer therapeutics. Along with synthetic approaches being tested to induce ACGs in cancer cells, naturally occurring compounds with a safe biological profile can be alternative options. In this context, riproximin with its already known substantial antineoplastic effects can be a lead compound for further development. However, further pre-clinical investigations are needed to understand events being operated by riproximin to induce ACGs. Declarations Conflicts of interest/Competing interests: The authors declare that they have no conflict of interest. Ethics approval: For clinical investigations, informed consent was obtained from all patients and the study was conducted in accordance with the guidelines of the Ethics Committee of the University of Health Sciences, Lahore, Pakistan. Furthermore, the procedures performed were in accordance with the ethical standards of the institutional and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Authors contributions: Asim Pervaiz conceived this study, supervised/analysed the experiments and wrote the manuscript draft. Nadia Naseem supervised clinical sampling/immunohistochemistry and drafted the relevant findings. Talha Saleem, Iqra Shaukat, Kinza Kanwal and Osheen Sajjad helped in transcriptomic expression profiling and preparation of figures. Syed Mohsin Raza performed the bioinformatic analysis of this study. Sana Iqbal, Faiza Shams and Bushra Ijaz provided the support in western blot analysis. Martin R. Berger facilitated the microarray experiments and also supervised the drafting/editing of the manuscript. Data availability statement: The datasets generated during the current study are available from the corresponding author upon reasonable request. Funding Source: No specific funding was acquired for this research work. References Grimm S, Noteborn M (2010) Anticancer genes: inducers of tumour-specific cell death signalling. 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J Cancer Res Clin Oncol. doi: 10.1007/s00432-022-04410-6 Tables Table 1A: Hormone receptor (HR) and Her2/neu status IHC Expression N (%) Combined HR status N (%) Molecular subtypes N (%) ER 20 (44.5%) ER+/PR+ 14 (31.1%) HR-/HER2+ (Her/2 type) 07 (15.5%) PgR 23(51.1%) ER-/PR+ 09 (20.0%) HR+/HER2+ (Luminal B) 08 (17.7%) Her2neu 21(46.6%) ER-/PR- 16 (35.5%) HR+/HER2- (Luminal A) 14 (31.2%) ER+/PR- 06 (13.3%) HR-/HER2- (Basal like) 16 (35.6%) Table 1B: NOXA, PAR-4 and TRAIL expression in N=45 cases NOXA IHC Expression N (%) PAR-4 IHC Expression N (%) TRAIL IHC Expression N (%) Neg/Low 21 (46.6%) PAR-4 - 27 (60.0%) TRAIL - 13 (28.8%) Elevated 24 (53.4%) PAR-4 + 18 (40.0%) TRAIL + 32 (71.2%) Table 1C: Molecular subtypes of breast cancer and expression of NOXA, PAR-4 and TRAIL Molecular subtypes NOXA Low/Neg NOXA Elevated PAR-4 - PAR-4 + TRAIL - TRAIL + P-value # HR+/HER2- 06 (42.8%) 08 (57.1%) 09 (64.2%) 05 (35.7%) 04 (28.5%) 10 (71.4%) 0.013 * HR+/HER2+ 04 (57.1%) 03 (42.8%) 04 (50.0%) 04 (50.0%) 02 (25.0%) 06 (75.0%) 0.883 HR-/HER2+ 02 (28.5%) 05 (71.4%) 03 (42.8%) 04 (57.1%) 03 (42.8%) 04 (57.1%) 0.093 HR-/HER2- 09 (56.2%) 07 (43.7%) 11 (68.7%) 05 (31.2%) 04 (25.0%) 12 (75.0%) 0.012* ER: Estrogen receptor, PR: Progesterone receptor, Her2: Human epidermal growth factor receptor 2, HR: Hormone receptors (ER, PR), Neg: Negative, N: Numbers Table 2A: Tumour Nottingham grades and expression of NOXA, PAR-4 and TRAIL Histological Grade NOXA Low/Neg NOXA Elevated PAR-4 - PAR-4 + TRAIL - TRAIL + P-value # 1 (n=8) 03 (37.5%) 05 (62.5%) 04 (50.0%) 04 (50.0%) 05 (62.5%) 03 (37.5%) 0.092 2 (n=16) 09 (56.2%) 07 (43.8%) 12 (75.0%) 04 (25.0%) 05 (31.3%) 11 (68.7%) 0.076 3 (n=21) 13 (61.9%) 08 (38.1%) 18 (85.7%) 03 (14.2%) 05 (23.8%) 16 (76.1%) 0.001* Total (N=45) 25 (55.5%) 20 (44.5%) 34 (75.6%) 11 (24.4%) 15 (33.4%) 30 (66.6%) Table 2B: Association between clinical stage groups and expression of NOXA, PAR-4 and TRAIL Clinical Stage NOXA Low/Neg NOXA Elevated PAR-4 - PAR-4 + TRAIL - TRAIL + P-value ^ I-II (n=22) 14 (63.6%) 08 (36.4%) 15 (68.2%) 07 (31.8%) 11 (50.0%) 11 (50.0%) 0.219 III-IV (n=23) 11 (47.8%) 12 (52.2%) 19 (82.6%) 04 (17.4%) 04 (17.4%) 19 (82.6%) 0.041* Total (N=45) 25 (55.5%) 20 (44.5%) 34 (75.6%) 11 (24.4%) 15 (33.4%) 30 (66.6%) #Chi-square test ^Pearson Chi-square Test. *Statistically significant association Cite Share Download PDF Status: Published Journal Publication published 01 May, 2023 Read the published version in Molecular Biology Reports → Version 1 posted Editorial decision: Major Revisions Needed 06 Mar, 2023 Reviewers agreed at journal 19 Jan, 2023 Reviewers invited by journal 16 Jan, 2023 Editor assigned by journal 12 Jan, 2023 First submitted to journal 10 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2466124","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":168074547,"identity":"d1fe0d9e-383a-4d36-a3ef-c295b74e373e","order_by":0,"name":"Asim Pervaiz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDCCA0CcAGdXQNgSQGxApJYzxGqBA8Y2IrTw3T78+MPDnDty5jOSHx78Oe9wnnwD88HbPAx3jHFpkTyXZiaRuO2ZscyNNIMDktsOFxscYEu25mF4ZoZLi8EZBjOGxG2HE2fwHDA4YAhkbGDgMZPmYThsg1sL++cPQC31M3iOfziQOOdw4vwG/m8EtPAYAB12OEGCvcfgwMGGw4kNB3jYQFpwOkzyDE8ZyC+GM9h7Cg42HEtP3HCYzdhyjsEznN7nO8O++ePPbXfkJZiBjB811onz25sf3nhTccewAZceCDiAxGYGO/gAVnU4tOASGQWjYBSMghELAPdyYebhhg3HAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2619-5304","institution":"University of Health Sciences Lahore","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Asim","middleName":"","lastName":"Pervaiz","suffix":""},{"id":168074548,"identity":"106851ed-edbe-41fd-8b75-94cdef2e50a8","order_by":1,"name":"Nadia Naseem","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Naseem","suffix":""},{"id":168074549,"identity":"79079ea8-a693-46ab-9c5e-34a096479cb6","order_by":2,"name":"Talha Saleem","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Talha","middleName":"","lastName":"Saleem","suffix":""},{"id":168074550,"identity":"99d74f5c-a668-4109-975e-a33001e9f04c","order_by":3,"name":"Syed Mohsin Raza","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Mohsin","lastName":"Raza","suffix":""},{"id":168074551,"identity":"afdf06f4-3290-4029-bbfa-90514eb533b4","order_by":4,"name":"Iqra Shaukat","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iqra","middleName":"","lastName":"Shaukat","suffix":""},{"id":168074552,"identity":"aed07d78-03d9-4e4b-a8b1-9b9f395cb9ec","order_by":5,"name":"Kinzah Kanwal","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kinzah","middleName":"","lastName":"Kanwal","suffix":""},{"id":168074553,"identity":"ab90d80b-bfb0-4f46-a565-ca5d00fc3443","order_by":6,"name":"Osheen Sajjad","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Osheen","middleName":"","lastName":"Sajjad","suffix":""},{"id":168074554,"identity":"d0456fa2-ec5d-43db-9a17-3c9846e0a50f","order_by":7,"name":"Sana Iqbal","email":"","orcid":"","institution":"University of Health Sciences Lahore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Iqbal","suffix":""},{"id":168074555,"identity":"1f970a35-da2f-4a94-a4d4-03873cce3749","order_by":8,"name":"Faiza Shams","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Faiza","middleName":"","lastName":"Shams","suffix":""},{"id":168074556,"identity":"a0bb06fe-d1ab-4035-91c4-65d01024cf58","order_by":9,"name":"Bushra Ijaz","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bushra","middleName":"","lastName":"Ijaz","suffix":""},{"id":168074557,"identity":"8c4edf7b-1318-4db6-a0a2-717e1bdc0e77","order_by":10,"name":"Martin R. Berger","email":"","orcid":"","institution":"German Cancer Research Centre: Deutsches Krebsforschungszentrum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"R.","lastName":"Berger","suffix":""}],"badges":[],"createdAt":"2023-01-11 08:24:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2466124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2466124/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11033-023-08477-3","type":"published","date":"2023-05-01T20:43:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":31755088,"identity":"1f354949-8d49-4c43-bd17-62e0ef4b3c6e","added_by":"auto","created_at":"2023-01-18 15:34:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":323396,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of ACGs in breast cancer patients. Histopathological expression levels of NOXA, PAR-4 and TRAIL genes were identified by immunohistochemistry in 45 breast cancer specimens. Low expression of the PAR-4 gene was found in 34/45 (76%) samples, while high TRAIL gene expression was identified in 30/45 (67%) tumor samples. An even distribution of NOXA expression (25/45=56% Neg/Low, 20/45=44% Elevated/High) was found in the breast cancer isolates.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2466124/v1/fe7f9bc65e277347913c1107.jpeg"},{"id":31756733,"identity":"37fdc744-903c-45fc-9509-eb00d47a4dd7","added_by":"auto","created_at":"2023-01-18 15:42:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28286,"visible":true,"origin":"","legend":"\u003cp\u003eRiproximin exposure and microarray analysis. MDA-MB-231 breast cancer cells were exposed to different concentrations of riproximin (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e, IC\u003csub\u003e75\u003c/sub\u003e) for 24-72 hours. Subsequent expression modifications were identified by cDNA microarray analysis. NOXA and PAR-4 genes were up-regulated in response to riproximin exposure, while TRAIL gene was persistently down-regulated during the whole experiment period.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2466124/v1/2eb5e726390f11f618bb974e.png"},{"id":31755087,"identity":"b93dcd1e-942b-4d4e-8971-9a377d86f1d0","added_by":"auto","created_at":"2023-01-18 15:34:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66272,"visible":true,"origin":"","legend":"\u003cp\u003eRiproximin mediated induction of the genes at transcriptome levels. Two breast cancer cell lines (MDA-MB-231 and MCF-7) were exposed to increasing concentrations of riproximin (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e or IC\u003csub\u003e75\u003c/sub\u003e) for 48 hours. \u003cstrong\u003ea.\u003c/strong\u003e Expression levels of the anticancer genes (NOXA, PAR-4, TRAIL) were identified via qRT-PCR while fold changes were calculated by 2-ΔΔCt method. NOXA and PAR-4 were consistently induced in the two breast cancer cell lines after riproximin exposure. TRAIL was moderately inhibited (\u0026lt;2fold) in MDA-MB-231 cells at lower concentrations of riproximin (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e), while persistently up-regulated in MCF-7 cells. \u003cstrong\u003eb.\u003c/strong\u003e Western blot analyses were performed to determine the expression levels of NOXA, PAR-4 and TRAIL genes. β-actin was used as a loading control and expressional changes were calculated using the\u0026nbsp;\u003cem\u003eImageJ\u003c/em\u003e\u0026nbsp;tool. A distinct increase of NOXA, PAR-4 and TRAIL protein was witnessed principally in MCF-7 cells at higher concentration (IC\u003csub\u003e50\u003c/sub\u003e) of riproximin.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2466124/v1/508097f463c7e354af8e602e.png"},{"id":31755089,"identity":"b945334c-16df-4a55-aaa2-c833261ea95a","added_by":"auto","created_at":"2023-01-18 15:34:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":593883,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatics analysis showed multidimensional role of anticancer genes. \u003cstrong\u003ea \u003c/strong\u003eCytoscape software and StringApp based representation of a physical network based on protein-protein interactions of ACGs. \u003cstrong\u003eb \u003c/strong\u003eGOChord plot of significant KEGG pathways related to cancer and cell signaling enriched by the ACGs based protein-protein interactionsnetwork.\u003cstrong\u003e c-e\u003c/strong\u003e GO barplot of top 20 significant Gene Ontology terms of the STRINGdb based physical network of protein-protein interactions of ACGs, including biological processes, molecular function and cell component.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-2466124/v1/0fdc0219e99af176471bd159.png"},{"id":44729352,"identity":"ba0da6e4-d2c2-43f0-9b07-4adc324c8b1b","added_by":"auto","created_at":"2023-10-16 21:15:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2466124/v1/ba540227-83bd-4e0c-a11e-46e8d352c1e0.pdf"}],"financialInterests":"","formattedTitle":"Anticancer genes (NOXA, PAR-4, TRAIL) are de-regulated in breast cancer patients and can be targeted by using a ribosomal inactivating plant protein (riproximin)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnticancer genes (ACGs) are endogenous adversaries of cancer cells, which can induce antineoplastic effects when expressed ectopically. So far, a few genes (~\u0026thinsp;10) have been assigned with the status of ACGs, while a systemic search for new members of this family is an on-going process [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Proteins translating from ACGs are complex in structures and interact specifically with their cellular counterparts to initiate cancer cell-specific death mechanisms. ACGs mediated antineoplastic effects include ER-stress, mitotic catastrophe, apoptosis and autophagy [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Currently, ACGs are being investigated actively for their potential role in cancer pathogenesis and therapeutic relevance. As per available data, low expression of ACGs is associated with poor prognosis, low survival rates, relapse of the disease conditions and resistance towards treatment modalities. Increased expression of ACGs is reported to sensitize cancer cells towards radio/chemotherapy and often induce synergistic effects in combination with the antineoplastic-agents [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As far as the therapeutic domain is concerned, most of the known ACGs are restricted to pre-clinical investigations till today. A predominant challenge is to find suitable delivery methods to have higher expression levels of these genes in cancer cells. In this context, ACG based immunotoxins, antibody conjugates and genetic engineering approaches are being tested [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Apart from these synthetic options, a highly neglected field is finding natural or synthetic compounds, which can induce the expression of ACGs in cancer cells. Identification of natural compounds with a safe physiological / biological profile is deemed crucial for exploiting ACGs \u0026ldquo;\u003cem\u003ethe Achilles Heel of cancer cell\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cem\u003eXimenia americana\u003c/em\u003e is a plant that grows in tropical and subtropical areas of African and American countries. Kernels of this plant have long been used in African traditional medicine by local healers as a treatment for cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Almost fifteen years ago, our group isolated and purified different protein fractions from aqueous extracts of \u003cem\u003eX. americana\u003c/em\u003e kernels and tested their corresponding anticancer properties. A protein fraction characterized by size of ~\u0026thinsp;60-kDa and substantial anticancer potential was identified and termed \u0026ldquo;\u003cem\u003eRiproximin\u003c/em\u003e\u0026rdquo; [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. DNA/peptide sequences showed that riproximin is a member of a big family consisting of ribosome-inactivating proteins (RIPs). RIPs are known for their catalytic nature and inhibit the translation procedure irreversibly in target cells by altering the 28S rRNA subunit [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As far as the molecular structure is concerned, riproximin is a heterodimer with two polypeptide chains (A- and B-chains) held together by an intermolecular disulphide bridge. The B-chain of riproximin has lectin-like properties and is responsible for binding to cell surface glycans, while the A-chain is accountable for subsequent catalytic activity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Riproximin has demonstrated substantial antineoplastic effects against a variety of cancer cell lines including breast, colorectal, leukaemia, pancreatic and prostate, while sparing normal healthy cells [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eIn vivo\u003c/em\u003e studies are also documented where riproximin showed anticancer effects in colorectal and pancreatic cancer liver metastasis rat models [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Reported antineoplastic effects of riproximin include ER-stress, induction of unfolded protein response, cell cycle arrest, apoptosis and autophagy. In addition, riproximin halts various functional aspects of cancer cells including proliferation, migration and colony forming abilities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we hypothesized that ACGs are differentially expressed in breast cancer and can be regulated via natural compounds for therapeutic purposes. To support this hypothesis, we identified the expressional levels of multiple ACGs (NOXA, PAR-4 and TRAIL) in 45 tumour samples of breast cancer via immunohistochemistry. Afterwards, the potential of riproximin to induce ACGs in two molecularly distinct breast cancer cell lines (MDA-MB-231 and MCF-7) was evaluated at transcriptome and proteome levels by using microarray, real-time PCR and western blot methodologies. Lastly, bioinformatic tools were applied to highlight the molecular/ functional importance of ACGs inside a cellular environment.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical samples and histopathological analysis\u003c/h2\u003e \u003cp\u003eA total of 45 female patients, 18\u0026ndash;55 years of age, presenting with primary breast cancers at the oncology units of Institute of Nuclear Medicine and Oncology (INMOL), Lahore, Services Hospital, Lahore and Allied Hospital, Faisalabad, Pakistan were included. After a complete radiological and histological diagnosis of malignancy on true-cut biopsy, patients were recruited for the study after obtaining their written informed consent. Patients with recurrence or on follow-up of malignancy were excluded. Modified radical mastectomy specimens were fixed in 10% neutral buffered formalin and grossly examined according to the College of American Pathologist (CAP) protocol. Paraffin embedded tissue sections were prepared and stained with H\u0026amp;E stain for confirmation of the histological diagnosis and grading was carried out following the Elston-Ellis modification of the \u003cem\u003eScarff\u003c/em\u003e-\u003cem\u003eBloom\u003c/em\u003e-\u003cem\u003eRichardson grading\u003c/em\u003e system [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry Of Noxa, Par-4 And Trail\u003c/h3\u003e\n\u003cp\u003eAbout 4\u0026ndash;6\u0026micro;m sections were taken on positively charged albumin coated slides for immunohistochemistry. The slides were placed in a hot air oven at 70\u0026deg;C for 30 minutes, followed by three washing steps in xylene, for 1 minute each. Paraffin removal was continued by three washing steps in alcohol, 1 minute each. Then, the sections were treated with H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 5 minutes to inhibit endogenous peroxidase and rehydrated by washing them in H\u003csub\u003e2\u003c/sub\u003eO. Epitope retrieval (unmasking of antigen) was done by using ethylenediaminetetraacetic acid (EDTA) buffer for 50 minutes at pH8 and 90\u0026deg;C. Slides were held at room temperature for 20 minutes and then washed in 1x PBS for 10 minutes. A blocking reagent was applied for 10 minutes and slides were again washed with PBS for 1 minute. Sections were incubated for 30 minutes with the primary antibodies for ER, PR, HER2neu, NOXA, PAR-4 and TRAIL antibodies. After washing in PBS, sections were incubated for 20 minutes with the secondary antibody and again washed with PBS. DAB was used as chromogen for 5\u0026ndash;8 minutes and again the slides were washed with PBS for 5 minutes. For counter staining, slides were dipped in haematoxylin and cleaned in xylene for 5 minutes. Finally, slides were mounted with DPX and examined under a microscope.\u003c/p\u003e \u003cp\u003eFor NOXA (cat#PA5-19977, Invitrogen), positive staining was localized to the tumor cell cytoplasm and was scored based on intensity (0/1/2/3) and percent tumor cell positivity [grouped into quartiles (0\u0026ndash;4)]. Intensity and immunopercent were multiplied to yield a final immune-score. For purposes of analysis, NOXA staining was dichotomized as negative/low versus elevated, where low was expanded to an immune-score of \u0026le;\u0026thinsp;4 while a score\u0026thinsp;\u0026gt;\u0026thinsp;4 was considered as an elevated expression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For PAR-4 staining (cat#PA5-77686, Invitrogen), immunohistochemical staining was assessed semi-quantitatively by multiplying both the intensity score (0/1/2/3) and the proportion of positive tumour cells (0, 0%; 1, 0\u0026ndash;10%; 2, 10\u0026ndash;50%; 3, 50\u0026ndash;100%), (maximum possible, 9). For the statistical analysis, the weighted scores were grouped into two categories where scores of 0\u0026ndash;3 were considered negative and 4\u0026ndash;9 positive. For TRAIL immunohistochemistry (cat#PA5-102584, Invitrogen), a binominal category was adopted. The absence of TRAIL expression was defined as the absence of cytoplasmic staining in less than 10% of tumour cells while the presence of staining in at least 10% of tumour cells was considered as evidence of TRAIL expression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For ER (cat#PA0151, Leica Biosystem) and PR (cat#PA0312, Leica Biosystem), Tumour Allred scoring scheme was adopted according to previously reported criteria [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A cut-off to define receptor positivity for ER and PR was an Allred score ⩾3, the internationally accepted cut-off. High scores were defined as Allred 6\u0026ndash;8, and low scores as Allred 3\u0026ndash;5. Regarding Her2 (cat#PA0983, Leica Biosystem) membrane staining, a previously published scoring scheme was adopted [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCell Culture And Riproximin\u003c/h3\u003e\n\u003cp\u003eTwo human breast cancer cell lines (MDA-MB-231 and MCF-7), obtained from American Type Culture Collection (ATCC, USA), were cultured in RPMI-1640 medium supplemented with L-glutamine (2 mM), foetal bovine serum (10%), streptomycin (100\u0026micro;g/ml) and penicillin (100IU/ml) under standard cell culture incubation conditions (5% CO2, 37˚C, humidified atmosphere). Cell lines were periodically checked (every 3 months) for mycoplasma contamination using VenorGem PCR kit (cat#11-1025, Minerva Biolabs) and passaged two to three times per week to maintain logarithmically growing cell populations. Riproximin was extracted and purified from the plant \u0026ldquo;\u003cem\u003eX. americana\u003c/em\u003e\u0026rdquo;, dissolved in 1x PBS and stored at -25\u003csup\u003e0\u003c/sup\u003eC as described previously [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eReal-time Pcr Analysis\u003c/h3\u003e\n\u003cp\u003eBreast cancer cell lines (MDA-MB-231 and MCF-7) were exposed to riproximin and expression modulations at transcriptome level were identified by qRT-PCR methodology for three ACGs (NOXA, PAR-4, TRAIL). To that purpose, the cell lines were cultured in 6-well culture plates (1.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well/2ml medium) and exposed for 48 hours to different inhibitory concentrations of riproximin (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e, IC\u003csub\u003e75\u003c/sub\u003e) selected based on our previous data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Following the exposure period, cells were harvested, cell pellets collected, total RNA extracted by a commercial kit (cat#K0731, Thermo-Fisher Scientific) and cDNA was synthesized (cat#K1622, Thermo-Fisher Scientific). Prepared samples were subjected to transcript detection of the ACGs by using a mixture of gene specific primers (NOXA: ATTACCGCTGGCCTACTGTG, CAATGTGCTGAGTTGGCACT, PAR-4: GCATGCACACTAAAAACCAAAA, TGTGTCCCAGTGTTATTCTTCAA, TRAIL: ACGACAAACAAATGGTCCAA, AGCTCAAATATTCCCCCTTGA) and SybrGreen/ROX master mix (cat#K0221, Thermo-Fisher Scientific) in a QuantStudio 3 real-time PCR system. All samples were processed in triplicate while expression levels of untreated cells were used as controls. Expression of a reference gene (HPRT1) was used to normalize the data, whereas fold changes were identified by the 2-△△Ct method.\u003c/p\u003e\n\u003ch3\u003eMicroarray Analysis\u003c/h3\u003e\n\u003cp\u003eMicroarray analysis was performed to determine the modifications in gene expression with minor modifications to our previously published protocol [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Briefly, MDA-MB-231 cells were exposed to riproximin (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e, IC\u003csub\u003e75\u003c/sub\u003e) for 24, 48 and 72 hours followed by RNA extraction with a RNeasy Mini kit (cat#74004, Qiagen). The quality of the extracted RNA was determined by using total RNA Nano chip assay on an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA samples with sufficient RNA Integrity Number values (\u0026ge;\u0026thinsp;8.8) were selected for the expression profiling.\u003c/p\u003e\n\u003ch3\u003eWestern Blot Analysis\u003c/h3\u003e\n\u003cp\u003eEffects of riproximin exposure on protein levels of ACGs (NOXA, PAR-4, TRAIL) in breast cancer cell lines were identified via western blot methodology. Briefly, the cells were cultured in 25cm\u003csup\u003e2\u003c/sup\u003e flasks (1.0\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/flask/5ml medium) and exposed to different concentrations of riproximin (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e) for 48 hours. Following the exposure period, cells pellets were collected and lysed with RIPA lysis buffer. Extracted proteins were quantified via Bradford assay and a total of 30\u0026micro;g protein/sample was subjected to electrophoresis on 4\u0026ndash;12% gradient polyacrylamide SDS gels. Afterwards, proteins were transferred onto nitrocellulose membrane and incubated for 2 hours at room temperature with specific primary antibodies for NOXA, PAR-4 or TRAIL (same as used for immunohistochemistry). Later on, membranes were rinsed with TBST buffer followed by incubation with alkaline-phosphatase (AP) conjugated secondary antibody for 1 hour at room temperature. Immunoreactive proteins were visualized with BCIP/NBT tablets (cat# B5655, Sigma), bands were analysed by using \u003cem\u003eImageJ\u003c/em\u003e software and levels of β-actin were used to normalize the protein expression data sets.\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eThe protein-protein interaction (PPI) network was created by importing the physical subnetwork of target genes from STRING via StringApp plugin into Cytoscape version 3.9.1. The maximum of 100 interactors with a minimum interaction score of 0.15 were imported to construct the network. Cytoscape plugin NetworkAnalyzer version 4.4.8 was used to assess the topological parameters. PPI network clusters was detected by Markov Cluster Algorithm (MCL) clustering using Clustermaker2 app version 2.2 in Cytoscape. Clustering was performed with inflation value of 3.0 and stringdb score as array source. The genes\u0026rsquo; list of the PPI network was imported into R and their corresponding ENTREZ IDs were fetched using \u0026ldquo;biomaRt\u0026rdquo; package version 2.52.0. Gene Ontology (GO) categories (Biological Process - BP, Molecular Function - MF and Cellular Component - CC) and KEGG pathways based functional enrichment was performed by \u0026ldquo;ClusterProfiler\u0026rdquo; package version 4.4.4 with the q-value cut off \u0026lt;\u0026thinsp;0.01. The three GO categories were visualized by \u0026ldquo;enrichplot\u0026rdquo; package version 1.16.2, while KEGG based enriched biological pathways were visualized by \u0026ldquo;GOplot\u0026rdquo; package version 1.0.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical diagnosis and molecular sub-types of breast cancer patients\u003c/h2\u003e \u003cp\u003eIn the current study, a total of 45 cases of adult female patients with breast lumps having complete radiological evaluation at the time of diagnosis were included. The age of these patients ranged from 23 to 52 years; the mean age was 44.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03 years. Taking age groups into consideration, n\u0026thinsp;=\u0026thinsp;20 (45.3%) patients were \u0026lt;\u0026thinsp;40 years of age and n\u0026thinsp;=\u0026thinsp;25 (55.5%) patients were \u0026ge;\u0026thinsp;40 years of age. The size of the tumor ranged from 1.8 to 21.5 cm with a mean value of 13.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 cm. Regarding the histological type, invasive ductal carcinoma (IDC) was diagnosed in all 45 cases. The Nottingham scoring and grading system showed n\u0026thinsp;=\u0026thinsp;8 (17.8%) cases in grade 1, n\u0026thinsp;=\u0026thinsp;16 (35.6%) cases in grade 2 and n\u0026thinsp;=\u0026thinsp;21 (46.6%) cases in grade 3. Clinical staging of these breast cancer cases was done according to American Joint Committee on Cancer (AJCC). There was n\u0026thinsp;=\u0026thinsp;7 cases (15.5%) in stage I, n\u0026thinsp;=\u0026thinsp;15 cases (33.3%) in stage II, n\u0026thinsp;=\u0026thinsp;18 cases (40%) in stage III and 5 cases (11.1%) in stage IV of breast carcinoma. For the purpose of analysis, the patients were subdivided into early (stage I/II) and advanced (III/IV) stage groups as 22 (48.8%) and 23 (51.2%), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). The molecular subtyping of breast cancer in increasing order of frequency were Her-2 type (HR-/HER2+), luminal B (HR+/HER2+), luminal A (HR+/HER2-) and basal like (HR-/HER2-), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferential Expression Of Anticancer Genes In Breast Cancer Tissues\u003c/h3\u003e\n\u003cp\u003eExpression frequency of NOXA, PAR-4 and TRAIL with respect to all samples included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and molecular subtypes of breast cancer in the current study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) was analysed in order to observe their statistical association. Histopathological expression levels of NOXA, PAR-4 and TRAIL genes were identified by immunohistochemistry. Examples of low or high expression are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC depict that higher expression of TRAIL and lower expression of PAR-4 correlated significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the luminal A and basal type of breast carcinomas, thus reflecting poor response to treatment and an unfavourable outcome. Expression of NOXA did not show any significant frequency distribution or correlation among the molecular subtypes of breast cancer. The cases of breast carcinoma with grade 1 showed higher frequency of TRAIL negative and NOXA elevated expression with no significant association. Grade 2 carcinomas showed a higher frequency of TRAIL positive, NOXA low/negative and PAR-4 negative expression with no significant association. Grade 3 cases demonstrated significant association with higher TRAIL expression and negative PAR-4 expression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When the clinical stage groups were compared with the expression of TRAIL, NOXA and PAR-4, only loss of PAR4 expression showed significant association with the advanced stage group while higher TRAIL expression was seen in the advanced tumour stage group without statistical significance. In contrast, NOXA neither showed any significant frequency distribution nor statistical association with the clinical stage groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTime And Concentration Dependant Effects Of Riproximin Exposure On Anticancer Genes\u003c/h3\u003e\n\u003cp\u003eEvaluation of the time and concentration dependant effects of antineoplastic agents is an important piece of information from clinical perspective. With this notion, we evaluated the impact of various concentrations (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e, IC\u003csub\u003e75\u003c/sub\u003e) of riproximin on ACGs during three different exposure periods (24, 48, 72 hours) in MDA-MB-231 cells via microarray strategy. The analysis showed concentration dependent induction of NOXA and PAR-4 genes during early exposure periods (24 and 48 hours), while this trend diluted overtime (72 hours period) with minimal alterations in the two genes when compared with untreated controls. TRAIL gene was consistently inhibited to moderate levels (\u0026lt;\u0026thinsp;2fold) in the cells during the whole experimental period as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRiproximin Mediated Induction Of Transcript/protein Of Anticancer Genes\u003c/h3\u003e\n\u003cp\u003eRiproximin is known for inducing anticancer effects and altering the expression of multiple genes in breast cancer cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, we particularly evaluated the impact of purified riproximin exposure on expressional changes at transcriptome and protein levels of the ACGs in two breast cancer cell lines. To that purpose, cell lines were exposed to riproximin (IC\u003csub\u003e25\u003c/sub\u003e-IC\u003csub\u003e75\u003c/sub\u003e) followed by qRT-PCR and western blot for expression analysis. At the transcriptome level, ACGs were upregulated by enlarge in the two cell lines with a distinct response towards riproximin treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Maximum induction in MDA-MB-231 cells was observed for NOXA (3.9fold), followed by PAR-4 (2.8fold) and TRAIL (1.5fold), respectively. In MCF-7 cells, TRAIL was the most up-regulated gene (9fold) followed by PAR-4 (8.9fold) and NOXA (3.4fold). Interestingly, maximum induction in the ACGs was observed in MDA-MB-231 cells when the highest concentration (IC\u003csub\u003e75\u003c/sub\u003e) of riproximin was applied. Opposite to this phenomenon, maximum induction of ACG transcripts was observed in MCF-7 cells with lower concentrations (IC\u003csub\u003e25\u003c/sub\u003e, IC\u003csub\u003e50\u003c/sub\u003e) of riproximin. As far as protein levels are concerned, a reasonable induction of NOXA was observed in MDA-MB-231 cells, while the other two ACGs (PAR-4 and TRAIL) were inhibited moderately. In MCF-7 cells, all the three ACGs were induced at protein levels at higher concentrations (IC\u003csub\u003e50\u003c/sub\u003e) of riproximin. By enlarge, expressional modifications in NOXA and TRAIL genes followed the similar pattern at transcriptome and proteome levels in the two cell lines following riproximin exposure. In contrast, PAR-4 gene was substantially induced at transcriptome levels, while inhibited (MDA-MB-231) or moderately up-regulated (MCF-7) at protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This, in turn indicates the presence of a potential negative feed-back mechanism creating hinderance in translation of PAR-4 transcripts or requirement of longer intervals to show proportional expression of this gene at protein levels. Over all, riproximin showed a considerable potential for up-regulating the expression of multiple ACGs in breast cancer cell-specific manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEffects Of Anticancer Genes On Multiple Molecular And Functional Arms\u003c/h3\u003e\n\u003cp\u003ePhysical interactions-based PPI network of each target gene was imported from STRING into Cytoscape separately and then merged to get the maximum number of interactors imported. The final merged network had 96 nodes with 761 edges having median degree of 15.84. MCL based clustering revealed 5 gene clusters, with the biggest cluster having 33 nodes with 171 edges while the smallest with 3 nodes and 2 edges. Interestingly, all the target genes were having independent clusters while two cluster arising from this network were devoid of target genes. It was also interesting to note that both DIABLO and TP53 shared interactions with NOXA and TRAIL based clusters as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Furthermore, the PPI network included 11 enzymes (ARF3, CASP10, CASP3, CASP6, CASP8, F2, HUWE1, PPP2CA, RAB2A, UCHL1 and ZDHHC17) 7 transcription factors (FOXO3, GFI1, GFI1B, KLF6, MYC, MYCN and TP53) and 3 kinases (PAK1, RIPK1 and BTK) where all the transcription factors and majority of the enzymes were included in the 3rd cluster. Among 38 interactors of TRAIL, 8 were significantly enriched in \u0026ldquo;TNFR/NGFR cysteine-rich region\u0026rdquo; protein families (pfam) domain (PF00020) while 8 were enriched in the \u0026ldquo;Death domain\u0026rdquo; pfam domain (PF00531). Furthermore, among 29 interactors of NOXA, 8 were enriched in the \u0026ldquo;Apoptosis regulator proteins, Bcl-2 family\u0026rdquo; pfam domain (PF00452). On the contrary, among the 27 interactors of PAR-4, 12 were enriched in \u0026ldquo;GGL domain\u0026rdquo; pfam domain (PF00631), 7 enriched in \u0026ldquo;G-protein alpha subunit\u0026rdquo; pfam domain (PF00503) and 7 in the \u0026ldquo;ADP-ribosylation factor family\u0026rdquo; pfam domain (PF00025).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn GO enrichment analysis significant enrichment of 440 biological processes (BP), 28 molecular functions (MF), 14 cellular components (CC), and 71 KEGG pathways was revealed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E). Interestingly, the majority of BP enriched were related to extrinsic and intrinsic apoptosis signalling pathways and their regulation (GO:0097191, GO:0097193 \u0026amp; GO:2001233). In the MF category major enriched terms were GTPase activity (GO:0003924) and G-protein beta-subunit binding (GO:0031681) with the of 21 and 13 genes, respectively. For the category of cell component (CC), PPI network was mainly enriched in heterotrimeric G-protein complex (GO:0005834), GTPase complex (GO:1905360) and the extrinsic component of cytoplasmic side of the plasma membrane (GO:0031234). KEGG pathway enrichment analysis revealed that beside being enriched in viral infections and pathways of cancer, the PPI network genes were enriched in a lot of key pathways such as PI3K-Akt signalling pathway, Apoptosis, Chemokine signalling pathway, Ras signalling pathway, TNF signalling pathway and Relaxin signalling pathway.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrecise targeting of the molecular factors associated with cancer pathogenesis is prerequisite for an effective cure. For this purpose, identification of novel therapeutic targets and development of corresponding molecules is a continuous process. ACGs are endogenous enemies of transformed cells and are being investigated for their prognostic and therapeutic relevance. Expressional profiling of various ACGs in cancers, their association with disease progression and involvement in treatment response is being studied by the scientific community. In parallel, novel delivery methods including antibody-based conjugates, immunotoxins development and genetic engineering strategies are being established to achieve ACGs mediated cancer cell-specific death. In addition to these synthetic strategies, naturally occurring compounds are also being considered for their potential to induce ACGs in cancer cells and to exploit resulting antitumor effects.\u003c/p\u003e \u003cp\u003eIn this study, as a first step, we identified the protein expression levels of three ACGs (NOXA, PAR-4, TRAIL) in breast cancer tissues by immunohistochemistry techniques (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To the best of our knowledge, this is the first study to investigate the expression profile of multiple ACGs at a time in breast cancer patients. At this time, we focused on IDC samples only, which is the most prevalent type of breast cancer. As far as the patient cohort is concerned, early onset of the disease (average\u0026thinsp;\u0026plusmn;\u0026thinsp;44 years age) with a considerable proportion of young patients (n\u0026thinsp;=\u0026thinsp;20/45: \u0026lt;40 years age) was noticed, which is mainly attributed to the lack of awareness programs and late diagnosis locally. Molecular subtyping revealed a maximum number of cases as triple negative (Basal like) followed by Luminal A type, which itself predicts poor prognosis of these patients. As far as immunohistochemistry results are concerned, distinct ACG expression profiles were identified in breast cancer patient samples. An almost even distribution of NOXA expression (Neg/Low 21/45\u0026thinsp;=\u0026thinsp;47%, Elevated 24/45\u0026thinsp;=\u0026thinsp;53%) was found with no correlation with the molecular subtypes of breast cancer. However, a clear trend was found between NOXA expression and tumor grade, where Neg/Low expression ratios increased with increasing grade of the disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This, in turn, reflects continuous inhibition of NOXA expression overtime in patients with advanced stages of breast cancer. Similar findings have also been reported where low expression of NOXA was found associated with poor prognosis, reduced overall survival and chemo-resistance in breast cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In our selected cohort, low expression of the PAR-4 gene was a dominant fact where 76% (34/45) of the samples were found negative for this ACG. Furthermore, a continuous decrease in PAR-4 expression was witnessed with the increasing severity of the disease. Precisely, 4/8 (50%), 12/16 (75%) and 18/21 (86%) cases of grade 1, 2 and 3 were found negative for PAR-4 expression, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The data is in line with other available reports where low expression of PAR-4 and its association with poor prognosis is reported in breast cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lower expression of PAR-4 was also correlated significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the luminal A and basal type of breast carcinomas, thus depicting the poor response to treatment and an unfavourable outcome. Similar findings have been reported, where PAR-4 expression was found to be low in the highly aggressive, estrogen-receptor negative (ER-), basal-like, and high-grade (grade 3) breast cancers, which are all associated with poor clinical outcome [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Alvarez and colleagues found lower PAR-4 expression associated with the poor response towards neoadjuvant chemotherapy and an increased risk of relapse in patients with breast cancer. Concurrently, higher expression of PAR-4 is also reported to sensitize triple-negative breast cancer cell lines to DNA damage-induced cell death by making cells more prone to chemo-sensitivity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. All in all, our data and available scientific reports suggest overall low expression of PAR-4 in breast cancer patients with a substantial association with aggressive nature, chemo-resistance and relapse of the disease. TRAIL is one of the most studied ACG till today and is being targeted in the field of cancer therapeutics. A major hurdle is to overcome the development of TRAIL resistance achieved by tumour cells. Despite these existing challenges, the development of specific molecules (agonists) against the TRAIL receptors and recombinant TRAIL is an on-going process. As far as the expression profile is concerned, TRAIL was the only gene where we found positive expression (at least 10% of tumour cells positively stained) in most of the clinical samples (32/45\u0026thinsp;=\u0026thinsp;71%, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This positivity of the expression was increased with the severity of the disease as shown by 3/8 (38%), 11/16 (69%) and 16/21 (76%) cases in grade 1, 2 and 3, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Higher levels of TRAIL correlated significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the luminal A and basal type of breast carcinomas. Grade 3 cases in the present study demonstrated a significant association with higher TRAIL expression and negative PAR-4 expression thus depicting that these two markers relate significantly with tumour aggressiveness and poor prognosis in breast cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Over and above to these facts, astonishing findings are being reported where combination of TRAIL along with platinum drugs (cisplatin) and liposomal formulations have shown to be effective against cancer stem cells and circulating breast cancer cells, which in turn, gives us hope that exploiting ACGs can be effective in controlling secondary tumour development and recurrence of the disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral natural compounds have shown the potential to up-regulate ACGs in cancer cells followed by induction of multidirectional antineoplastic effects [\u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As we know, by enlarge naturally occurring agents bear a safe physiological profile, thus exploiting such compounds to up-regulate ACGs in cancer cells seems to be an attractive research domain. Among the plant-based anticancer agents, RIPs comprise an important class of toxins with the promising capacity to be developed as therapeutic entities [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The potential effects of RIPs\u0026rsquo; exposure on endogenous enemies of cancer cells (ACGs) are a neglected field so far. In a previous study, we reported that riproximin can induce significantly (˃100fold) the expression of a well-known ACG (MDA-7/IL24) in breast cancer cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this particular study, we were interested to figure out the potential impact of riproximin on expressional profile of three further ACGs (NOXA, PAR-4, TRAIL). For this purpose, two breast cancer cell lines with distinct molecular subtypes (MDA-MB-231: triple negative, MCF-7: ER/PR positive) were selected and exposed to various concentrations of riproximin. Transcriptome and proteomic data revealed that riproximin can up-regulate the three ACGs in breast cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparatively, more prominent induction of the three ACGs was observed in MCF-7 (up to 3.3fold NOXA, 8.9fold PAR-4, 9fold TRAIL) as compared to MDA-MB-231 cells (up to 3.8fold NOXA, 2.8fold PAR-4, 1.5fold TRAIL). In a previous study, we reported that riproximin can induce significant cytotoxic effects in MCF-7 cells after 48 hours of exposure at much lower concentrations (IC50:0.38ng/ml) as compared to MDA-MB-231 (IC50:3.6ng/ml) cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Based on previous data and current findings, it can be concluded that breast cancer cells with particular molecular sub-type (e.g., ER/PR positive status) are more sensitive towards riproximin exposure. One potential explanation could be that riproximin interacts with the cells through its lectin binding domains (B-chain) preferably to cell surface glycans (NA2/NA3) and N-acetyl-D-galactosamine (GalNAc/Tn Antigen) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, the presence and saturation status of specific glyco-targets along with the potential involvement of other cell surface receptors can modify the subsequent binding capacity and internalization of riproximin. Other than this, the possibility of wide-ranged downstream signalling cascades being targeted by riproximin in a cell-specific manner cannot be ruled out. As far as riproximin mediated induction of ACGs is concerned, the phenomenon is not restricted to breast cancer cells only, as we recently witnessed that this plant protein can induce the ACGs in multiple colorectal cancer cell lines [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. To summarize, riproximin mediated up-regulation of ACGs can be exploited against various malignancies provided with further detailed pre-clinical investigations.\u003c/p\u003e \u003cp\u003eBioinformatics is an emerging interdisciplinary field being exploited in modern day biology to understand complex cellular mechanisms including roles of potential genes/proteins within a cell and extrapolation of the wet-lab results. With this notion, we were interested to highlight the molecular significance of ACGs in cell environment by using various \u003cem\u003ein silico\u003c/em\u003e approaches. In the PPI based clustering built with the help of STRING and Cytoscape software, the ACGs showed independent clusters, which in turn, reflects their minimal interdependency on each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). If so, this fact is of particular importance as it shows up-regulation of the ACGs via riproximin can initiate distinct and independent cascades to induce antineoplastic effects via multiple molecular directions. TP53 (a master regulator of apoptosis) and DIABLO (a proapoptotic mitochondrial protein) were the only two genes connected between NOXA and TRAIL clusters. The substantial involvement of NOXA and TRAIL in apoptotic routes is a known fact, thus linking bridges between these two ACGs via TP53 and DIABLO is not a surprising observation. PPI networks of the ACGs also showed presence of key enzymes, transcription factors and kinases in the clusters, which means ACGs are involved in mechanistic routes responsible for enzymatic activities, gene expression regulations and phosphorylation steps, hence bear a wide-ranged responsibility inside cells. GO enrichment analysis revealed a huge number of BP (440), MF (28), CC (14) and KEGG pathways (71) being associated with the ACGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E). Mainly these CC, MF and BP include GTPase complex formation, their corresponding activities and apoptotic signalling cascades, respectively. KEGG pathways data was also very enriched where ACGs were shown to be involved in vital mechanisms including survival (PI3K/Akt), apoptosis/necroptosis (caspases and BCL family), chemokine networking, TNF and RAS/RAF signalling cascades. All in all, the bioinformatic approach showed that the ACGs are extremely crucial factors involved in multidirectional aspects of a cell life and need due attention to explore their corresponding prognostic/therapeutic relevance in cancers.\u003c/p\u003e \u003cp\u003eOverall, the findings of this study reflected a distinct expression pattern of ACGs in breast cancer clinical isolates. Targeting these endogenous enemies of transformed cells can be instrumental in the field of cancer therapeutics. Along with synthetic approaches being tested to induce ACGs in cancer cells, naturally occurring compounds with a safe biological profile can be alternative options. In this context, riproximin with its already known substantial antineoplastic effects can be a lead compound for further development. However, further pre-clinical investigations are needed to understand events being operated by riproximin to induce ACGs.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e For clinical investigations, informed consent was obtained from all patients and the study was conducted in accordance with the guidelines of the\u0026nbsp;Ethics Committee of the University of Health Sciences, Lahore, Pakistan.\u0026nbsp;Furthermore, the procedures performed were in accordance with the ethical standards of the institutional and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions:\u0026nbsp;\u003c/strong\u003eAsim Pervaiz conceived this study, supervised/analysed the experiments and wrote the manuscript draft. Nadia Naseem supervised clinical sampling/immunohistochemistry and drafted the relevant findings. Talha Saleem, Iqra Shaukat, Kinza Kanwal and Osheen Sajjad helped in transcriptomic expression profiling and preparation of figures. Syed Mohsin Raza performed the bioinformatic analysis of this study. Sana Iqbal, Faiza Shams and Bushra Ijaz provided the support in western blot analysis. Martin R. Berger facilitated the microarray experiments and also supervised the drafting/editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The datasets generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source:\u003c/strong\u003e No specific funding was acquired for this research work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGrimm S, Noteborn M (2010) Anticancer genes: inducers of tumour-specific cell death signalling. 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Toxins (Basel) 2(11):2699\u0026ndash;2737. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/toxins2112699\u003c/span\u003e\u003cspan address=\"10.3390/toxins2112699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePervaiz A, Saleem T, Kanwal K, Raza SM, Iqbal S, Zepp M, Georges RB, Berger MR (2022) Expression profiling of anticancer genes in colorectal cancer patients and their in vitro induction by riproximin, a ribosomal inactivating plant protein. J Cancer Res Clin Oncol. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00432-022-04410-6\u003c/span\u003e\u003cspan address=\"10.1007/s00432-022-04410-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1A: Hormone receptor (HR) and Her2/neu status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIHC Expression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined HR status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e20 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003eER+/PR+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e14 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003eHR-/HER2+\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Her/2 type)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e07 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003ePgR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e23(51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003eER-/PR+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e09 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003eHR+/HER2+\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Luminal B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e08 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003eHer2neu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e21(46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003eER-/PR-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e16 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003eHR+/HER2-\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Luminal A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e14 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003eER+/PR-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e06 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003eHR-/HER2-\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Basal like)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e16 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1B: NOXA, PAR-4 and TRAIL expression in N=45 cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA IHC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExpression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 IHC Expression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL IHC Expression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeg/Low\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e21 (46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e27 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e13 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"22.809667673716014%\"\u003e\n \u003cp\u003e\u003cstrong\u003eElevated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e24 (53.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"21.45015105740181%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"11.329305135951662%\"\u003e\n \u003cp\u003e18 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.6012084592145%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\"\u003e\n \u003cp\u003e32 (71.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1C: Molecular subtypes of breast cancer and expression of NOXA, PAR-4 and TRAIL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA Low/Neg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA Elevated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.515151515151516%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003csup\u003e#\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003eHR+/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e06 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e08 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\"\u003e\n \u003cp\u003e09 (64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e05 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e04 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\"\u003e\n \u003cp\u003e10 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.515151515151516%\"\u003e\n \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003eHR+/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e04 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e03 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\"\u003e\n \u003cp\u003e04 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e04 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e02 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\"\u003e\n \u003cp\u003e06 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.515151515151516%\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003eHR-/HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e02 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e05 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\"\u003e\n \u003cp\u003e03 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e04 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e03 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\"\u003e\n \u003cp\u003e04 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.515151515151516%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003eHR-/HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e09 (56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e07 (43.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\"\u003e\n \u003cp\u003e11 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e05 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.575757575757576%\"\u003e\n \u003cp\u003e04 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\"\u003e\n \u003cp\u003e12 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.515151515151516%\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eER:\u0026nbsp;Estrogen receptor, PR: Progesterone receptor, Her2: Human epidermal growth factor receptor 2, HR: Hormone receptors (ER, PR), Neg: Negative, N: Numbers\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"667\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2A: Tumour Nottingham grades and expression of NOXA, PAR-4 and TRAIL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eHistological Grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA Low/Neg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA Elevated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003csup\u003e#\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003e1 (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e03 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e05 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e04 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e04 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e05 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e03 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003e2 (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e09 (56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e07 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e12 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e04 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e05 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e11 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003e3 (n=21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e13 (61.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e08 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e18 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e03 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e05 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e16 (76.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003eTotal (N=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e25 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e20 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e34 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e11 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e15 (33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e30 (66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2B: Association between clinical stage groups and expression of NOXA, PAR-4 and TRAIL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Stage\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA Low/Neg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOXA Elevated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR-4 +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRAIL +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003csup\u003e^\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003eI-II (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e14 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e08 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e15 (68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e07 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e11 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e11 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003eIII-IV (n=23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e11 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e12 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e19 (82.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e04 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e04 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e19 (82.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.874251497005988%\"\u003e\n \u003cp\u003eTotal (N=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e25 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e20 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e34 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e11 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e15 (33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.724550898203592%\"\u003e\n \u003cp\u003e30 (66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.778443113772456%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e#Chi-square test\u003c/p\u003e\n\u003cp\u003e^Pearson Chi-square Test.\u003c/p\u003e\n\u003cp\u003e*Statistically significant association\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast cancer, Anticancer genes, Riproximin, Plant protein","lastPublishedDoi":"10.21203/rs.3.rs-2466124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2466124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Anticancer genes are endogenous enemies of transformed cells and impose antineoplastic effects upon ectopic expression. Identifying the expression profile of these genes is a prerequisite to explore their prognostic and therapeutic relevance in cancers. In parallel, natural compounds can be explored for their ability to upregulate anticancer genes in malignant cells for therapeutic purposes. In this study, we identified the expression levels of anticancer genes in breast cancer clinical isolates. In addition, the potential of a purified and sequenced plant protein (riproximin) to induce anticancer genes in breast cancer cells was evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003eExpression profiles of three anticancer genes (NOXA, PAR-4, TRAIL) were identified by immunohistochemistry in 45 breast cancer clinical isolates. Effects of riproximin exposure on expression of the anticancer genes were explored via microarray, real-time PCR and western blot methodologies. Lastly, the bioinformatic approach was adopted to highlight the molecular/functional significance of the anticancer genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eNOXA expression was evenly de-regulated among the clinical isolates, while PAR-4 was significantly down-regulated in majority of the breast cancer tissues. In contrast, a higher TRAIL expression was observed in most of the clinical samples. Expression levels of the anticancer genes were following a distinct trend in accordance with the disease severity. Riproximin showed a substantial potential of inducing the anticancer genes in breast cancer cells at transcriptomic and protein levels. The bioinformatic approach revealed involvement of anticancer genes in multiple cellular functions and signaling cascades.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eAnticancer genes were de-regulated and showed discrete expression patterns in breast cancer patient samples. Riproximin effectively induced the expression of selected anticancer genes in breast cancer cells.\u003c/p\u003e","manuscriptTitle":"Anticancer genes (NOXA, PAR-4, TRAIL) are de-regulated in breast cancer patients and can be targeted by using a ribosomal inactivating plant protein (riproximin)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-18 15:34:31","doi":"10.21203/rs.3.rs-2466124/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2023-03-06T05:01:37+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-01-19T19:18:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-01-16T11:22:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-12T15:49:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2023-01-11T03:24:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b8c71201-bfd8-4043-afba-3e1a20b25920","owner":[],"postedDate":"January 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:02:11+00:00","versionOfRecord":{"articleIdentity":"rs-2466124","link":"https://doi.org/10.1007/s11033-023-08477-3","journal":{"identity":"molecular-biology-reports","isVorOnly":false,"title":"Molecular Biology Reports"},"publishedOn":"2023-05-01 20:43:03","publishedOnDateReadable":"May 1st, 2023"},"versionCreatedAt":"2023-01-18 15:34:31","video":"","vorDoi":"10.1007/s11033-023-08477-3","vorDoiUrl":"https://doi.org/10.1007/s11033-023-08477-3","workflowStages":[]},"version":"v1","identity":"rs-2466124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2466124","identity":"rs-2466124","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}