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MUFA, monounsaturated fatty acids; SFA, saturated fatty acids; SCD1, stearoyl-CoA desaturase 1; MIF, macrophage migration inhibitory factor; CXCR, CXC chemokine receptor; PI3K, phosphoinositide 3-kinase; MAPK, extracellular signal-regulated kinase; ERK, mitogen-activated protein kinase.
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The diagnosis and treatment of soft tissue sarcomas (STSs) has been particularly difficult, because STSs are a group of highly heterogeneous tumors in terms of histopathology, histological grade, and primary site. Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of ne...
Citations
... Stearoyl-CoA desaturase 1 SCD1 is highly expressed in multiple tumour types [88][89][90][91] and appears to participate in the biology of ccRCC as well 39 . SCD1 produces MUFAs from saturated FAs (SFAs, such as palmitate) to be incorporated into glycerophospholipids and sphingolipids for cell membranes, or serve as signalling molecules 92 (Fig. 3). ...
Lipid droplet formation is a defining histological feature in clear-cell renal cell carcinoma (ccRCC) but the underlying mechanisms and importance of this biological behaviour have remained enigmatic. De novo fatty acid (FA) synthesis, uptake and suppression of FA oxidation have all been shown to contribute to lipid storage, which is a necessary tumour adaptation rather than a bystander effect. Clinical studies and mechanistic investigations into the roles of different enzymes in FA metabolism pathways have revealed new metabolic vulnerabilities that hold promise for clinical effect. Several metabolic alterations are associated with worse clinical outcomes in patients with ccRCC, as lipogenic genes drive tumorigenesis. Enzymes involved in the intrinsic FA metabolism pathway include FA synthase, acetyl-CoA carboxylase, ATP citrate lyase, stearoyl-CoA desaturase 1, cluster of differentiation 36, carnitine palmitoyltransferase 1A and the perilipin family, and each might be potential therapeutic targets in ccRCC owing to the link between lipid deposition and ccRCC risk. Adipokines and lipid species are potential biomarkers for diagnosis and treatment monitoring in patients with ccRCC. FA metabolism could potentially be targeted for therapeutic intervention in ccRCC as small-molecule inhibitors targeting the pathway have shown promising results in preclinical models. Altered lipid metabolism is a visually obvious, distinguishing feature of clear-cell renal cell carcinoma. In this Perspective the authors describe the molecular mechanisms driving fatty acid accumulation and discuss potential therapeutic targets in this disease.
... The overexpression of MIF in human sarcoma samples also is well-documented. 119,123 The majority of the current literature suggests that MIF induces sarcoma vascularization, growth and metastasis. ...
Sarcomas are defined as a group of mesenchymal malignancies with over 100 heterogeneous subtypes. As a rare and difficult to diagnose entity, micrometastasis is already present at the time of diagnosis in many cases. Current treatment practice of sarcomas consists mainly of surgery, (neo)adjuvant chemo- and/or radiotherapy. Although the past decade has shown that particular genetic abnormalities can promote the development of sarcomas, such as translocations, gain-of-function mutations, amplifications or tumor suppressor gene losses, these insights have not led to established alternative treatment strategies so far. Novel therapeutic concepts with immunotherapy at its forefront have experienced some remarkable success in different solid tumors while their impact in sarcoma remains limited. In this review, the most common immunotherapy strategies in sarcomas, such as immune checkpoint inhibitors, targeted therapy and cytokine therapy are concisely discussed. The programmed cell death (PD)-1/PD-1L axis and apoptosis-inducing cytokines, such as TNF-related apoptosis-inducing ligand (TRAIL), have not yielded the same success like in other solid tumors. However, in certain sarcoma subtypes, e.g. liposarcoma or undifferentiated pleomorphic sarcoma, encouraging results in some cases when employing immune checkpoint inhibitors in combination with other treatment options were found. Moreover, newer strategies such as the targeted therapy against the ancient cytokine macrophage migration inhibitory factor (MIF) may represent an interesting approach worth investigation in the future.
... Overexpression of SCD1 had been reported in many malignant cells, and upregulated levels of SCD1 activity have also been associated with the change of certain aspects of tumor cell behavior, such as tumor cell growth and proliferation [12,13]. Furthermore, the prognostic significance of SCD1 expression was also revealed in many cancers, such as breast cancer, lung adenocarcinoma, colon cancer and soft tissue sarcomas, which showed that high expression of SCD1 was related to a poor outcome for patients with cancers [14][15][16][17]. Recent studies had also reported that SCD1 might be a novel molecular therapeutic target for ccRCC [18,19]. ...
Stearoyl-CoA desaturase 1 (SCD1), the rate-limiting enzymes in the biosynthesis of monounsaturated fatty acids from saturated fatty acids, have been gradually recognized as a potential therapeutic target for various malignancies, particularly in clear-cell renal cell carcinoma (ccRCC). However, the prognostic value of SCD1 in ccRCC is still unknown. The aim of this study is to evaluate the clinical significance of SCD1 expression in patients with ccRCC. SCD1 expression in tumor tissues obtained from 359 patients who underwent nephrectomy for ccRCC are retrospectively assessed. During a median follow-up of 63 months (range: 1–144month), 56 patients in total died before the last follow-up in this study. Survival curves were plotted with the Kaplan–Meier method and compared with the log-rank test. Meanwhile, univariate and multivariate Cox regression models were applied to evaluate the prognostic value of SCD1 expression in overall survival (OS) for ccRCC patients. Moreover, SCD1 was enrolled into a newly built nomogram with factors selected by multivariate analysis, and the calibration was built to evaluate the predictive accuracy of nomogram. High SCD1 expression occurred in 61.6% (221/359) of ccRCC patients, which was significantly associated with age (p = 0.030), TNM stage (p = 0.021), pN stage (p = 0.014), Fuhrman grade (p = 0.014) and tumor sizes (p = 0.040). In multivariate analysis, SCD1 expression was confirmed as an adverse independent prognostic factor for OS. The prognostic accuracy of TNM stage, Fuhrman grade and tumor sizes was significantly increased when SCD1 expression was added. The independent prognostic factors, pT stage, pN stage, Fuhrman grade and tumor sizes, as well as SCD1 expression were integrated to establish a predictive nomogram with high predictive accuracy. Calibration curves revealed optimal consistency between observations and prognosis. In conclusion, high SCD1 expression is an independent prognostic factor for OS in patients with ccRCC. Our data suggest that the expression of SCD1 might guide the clinical decisions for patients with ccRCC.
... For example, using gene expression data Takahashi et al. reported MIF in combination with stearoyl-CoA desaturase 1, to be a marker for the differential diagnosis of soft tissue sarcoma (STS) subtypes (UPS and MFS) [32]. In a follow-up study, it was reported that this gene expression signature can be used as a prognostic biomarker for STS [33]. It is also intriguing that the diagnostic protein identified by Takahashi et al. (stearoyl-CoA desaturase 1) and our study (acyl-CoA-binding protein) are both involved in the processing of fatty acids. ...
... Prognostic biomarkers not only enable the stratification of patients, they may also provide important insights into tumor progression that may ultimately lead to novel treatment strategies. For example, Takahashi et al. provide a hypothetical regulation model for metabolic and signaling control from the prognostic biomarkers found in soft tissue sarcomas [33]. In another example, it was demonstrated that a biomarker that could predict patient response to neoadjuvant chemotherapy in oesophageal adenocarcinoma was linked to mitochondrial defects [13]; interestingly these same biomarkers also differentiated poor survival patients. ...
The combination of high heterogeneity, both intra-tumoral and inter-tumoral, with their rarity has made diagnosis, prognosis of high grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study we investigated the ability of MALDI-MSI of proteins to distinguish between high grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11,162), macrophage migration inhibitory factor (m/z 12,350), thioredoxin (m/z 11,608) and galectin-1 (m/z 14,633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9,753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11,314 and 11,355), indicative of poor survival for LMS patients. This article is protected by copyright. All rights reserved.
... The usability of our combined method was confirmed by applying it into another dataset [15]. In general, the objective of statistical or bioinformatics analysis is the enrichment of important information from a large dataset [16][17][18][19][20][21][22][23][24][25]. The use of a knowledge-based algorithm is not a novel concept, but is both practical and useful [26][27][28][29][30][31][32][33][34][35][36]. ...
Background:
Variability in drug response between individual patients is a serious concern in medicine. To identify single-nucleotide polymorphisms (SNPs) related to drug response variability, many genome-wide association studies have been conducted.
Methods:
We previously applied a knowledge-based bioinformatic approach to a pharmacogenomics study in which 119 fluoropyrimidine-treated gastric cancer patients were genotyped at 109,365 SNPs using the Illumina Human-1 BeadChip. We identified the SNP rs2293347 in the human epidermal growth factor receptor (EGFR) gene as a novel genetic factor related to chemotherapeutic response. In the present study, we reanalyzed these hypothesis-free genomic data using extended knowledge.
Results:
We identified rs2867461 in annexin A3 (ANXA3) gene as another candidate. Using logistic regression, we confirmed that the performance of the rs2867461 + rs2293347 model was superior to those of the single factor models. Furthermore, we propose a novel integrated predictive index (iEA) based on these two polymorphisms in EGFR and ANXA3. The p value for iEA was 1.47 × 10(-8) by Fisher's exact test. Recent studies showed that the mutations in EGFR is associated with high expression of dihydropyrimidine dehydrogenase, which is an inactivating and rate-limiting enzyme for fluoropyrimidine, and suggested that the combination of chemotherapy with fluoropyrimidine and EGFR-targeting agents is effective against EGFR-overexpressing gastric tumors, while ANXA3 overexpression confers resistance to tyrosine kinase inhibitors targeting the EGFR pathway.
Conclusions:
These results suggest that the iEA index or a combination of polymorphisms in EGFR and ANXA3 may serve as predictive factors of drug response, and therefore could be useful for optimal selection of chemotherapy regimens.
... All patients provided written informed consent. [51], as shown in Table S1. Tumor samples were obtained at the time of excision and were cryopreserved in liquid nitrogen. ...
... We previously proposed a statistical simulation based on a permutation test and the integration of multiple statistics [51]. ...
... We also calculated Spearman's rank correlation coefficients to assess the relationships between gene expression signals and histological grades [54] or incidence of tumor metastases. We considered data obtained after 50 months of follow-up as censored data in the analysis of the logrank test, similar to the procedure followed in our previous study [51]. Kaplan-Meier curves [55] based on histological subtype were constructed for all STS patients. ...
The diagnosis and treatment of soft tissue sarcomas (STS) have been difficult. Of the diverse histological subtypes, undifferentiated pleomorphic sarcoma (UPS) is particularly difficult to diagnose accurately, and its classification per se is still controversial. Recent advances in genomic technologies provide an excellent way to address such problems. However, it is often difficult, if not impossible, to identify definitive disease-associated genes using genome-wide analysis alone, primarily because of multiple testing problems. In the present study, we analyzed microarray data from 88 STS patients using a combination method that used knowledge-based filtering and a simulation based on the integration of multiple statistics to reduce multiple testing problems. We identified 25 genes, including hypoxia-related genes (e.g., MIF, SCD1, P4HA1, ENO1, and STAT1) and cell cycle- and DNA repair-related genes (e.g., TACC3, PRDX1, PRKDC, and H2AFY). These genes showed significant differential expression among histological subtypes, including UPS, and showed associations with overall survival. STAT1 showed a strong association with overall survival in UPS patients (logrank p = 1.84×10-6 and adjusted p value 2.99×10-3 after the permutation test). According to the literature, the 25 genes selected are useful not only as markers of differential diagnosis but also as prognostic/predictive markers and/or therapeutic targets for STS. Our combination method can identify genes that are potential prognostic/predictive factors and/or therapeutic targets in STS and possibly in other cancers. These disease-associated genes deserve further preclinical and clinical validation.
... In general, the objective of a statistical or bioinformatic analysis is the enrichment of important information in a large dataset [42][43][44][45][46][47]. The use of a knowledge-based algorithm is not a novel concept, but is both practical and useful [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66]. In the previous study, we found that rs2293347 in the gene of human epidermal growth factor receptor (EGFR) is a candidate SNP related to the chemotherapeutic response; we achieved this result by applying our combined method to gastric cancer patients who were treated with fluoropyrimidine [7]. ...
Interindividual variation in a drug response among patients is known to cause serious problems in medicine. Genomic information has been proposed as the basis for "personalized" health care. The genome-wide association study (GWAS) is a powerful technique for examining single nucleotide polymorphisms (SNPs) and their relationship with drug response variation; however, when using only GWAS, it often happens that no useful SNPs are identified due to multiple testing problems. Therefore, in a previous study, we proposed a combined method consisting of a knowledge-based algorithm, 2 stages of screening, and a permutation test for identifying SNPs. In the present study, we applied this method to a pharmacogenomics study where 109,365 SNPs were genotyped using Illumina Human-1 BeadChip in 168 cancer patients treated with irinotecan chemotherapy. We identified the SNP rs9351963 in potassium voltage-gated channel subfamily KQT member 5 (KCNQ5) as a candidate factor related to incidence of irinotecan-induced diarrhea. The p value for rs9351963 was 3.31×10-5 in Fisher's exact test and 0.0289 in the permutation test (when multiple testing problems were corrected). Additionally, rs9351963 was clearly superior to the clinical parameters and the model involving rs9351963 showed sensitivity of 77.8% and specificity of 57.6% in the evaluation by means of logistic regression. Recent studies showed that KCNQ4 and KCNQ5 genes encode members of the M channel expressed in gastrointestinal smooth muscle and suggested that these genes are associated with irritable bowel syndrome and similar peristalsis diseases. These results suggest that rs9351963 in KCNQ5 is a possible predictive factor of incidence of diarrhea in cancer patients treated with irinotecan chemotherapy and for selecting chemotherapy regimens, such as irinotecan alone or a combination of irinotecan with a KCNQ5 opener. Nonetheless, clinical importance of rs9351963 should be further elucidated.
Prostate cancer (PCa) is a common type of cancer affecting male population. PCa treatments have side effects and are temporarily effective, so new therapeutic options are being investigated. Due to the high demand of energy for cell proliferation, an increase in the expression and activity of lipogenic enzymes such as the stearoyl-CoA desaturase (SCD) have been observed in PCa. Sterculic acid, contained in the seed's oil of Malvales, is a natural inhibitor of SCD. The objective of our investigation was to evaluate the effects of sterculic oil (SO) from Sterculia apetala seeds on proliferation, cell cycle and apoptosis in prostate cancer cells. SO was administered to PC3 and LNCaP cells, and to prostate normal cells; cell viability, cell cycle, apoptosis, SCD gene and protein expression and enzymatic activity were analyzed. SO administration (4 mM sterculic acid) diminished cell viability in LNCaP and PC3 cells, arrested cell cycle in G2 and promoted apoptosis. SO diminished SCD enzymatic activity with no effects on gene nor protein expression. Our results suggest that SO might offer benefits as an adjuvant in hormonal and chemotherapy prostate cancer treatments. This is the first study to analyze the effect of SO on cancer cells.
Drug repurposing is a cost effective means of targeting new therapies for cancer. We have examined the effects of the repurposed drugs, bezafibrate, medroxyprogesterone acetate and valproic acid on human osteosarcoma cells, i.e., SAOS2 and MG63 compared with their normal cell counterparts, i.e. mesenchymal stem/stromal cells (MSCs). Cell growth, viability and migration were measured by biochemical assay and live cell imaging, whilst levels of lipid-synthesising enzymes were measured by immunoblotting cell extracts. These drug treatments inhibited the growth and survival of SAOS2 and MG63 cells most effectively when used in combination (termed V-BAP). In contrast, V-BAP treated MSCs remained viable with only moderately reduced cell proliferation. V-BAP treatment also inhibited migratory cell phenotypes. MG63 and SAOS2 cells expressed much greater levels of fatty acid synthase and stearoyl CoA desaturase 1 than MSCs, but these elevated enzyme levels significantly decreased in the V-BAP treated osteosarcoma cells prior to cell death. Hence, we have identified a repurposed drug combination that selectively inhibits the growth and survival of human osteosarcoma cells in association with altered lipid metabolism without adversely affecting their non-transformed cell counterparts.
This chapter starts to focus on the applications of clustering algorithms rather than the techniques; that is, what is the information that one can obtain from massive biological data using clustering algorithms rather than how. It discusses the applications of hierarchical clustering algorithms, e.g. human breast cancer classification. The chapter discusses some applications that employed fuzzy clustering in the bioinformatics field. It introduces a self-organising map (SOM), which is one of the most famous and popular neural network-based clustering algorithms. The chapter focuses on mixture model clustering, which is widely used in bioinformatics cluster analysis. It details applications of graph-based clustering. The yeast protein-interaction networks were represented as graphs of vertices and edges, corresponding to proteins and interactions, respectively. Obtaining a consensus set of clusters from a number of clustering methods may improve confidence in gene expression analysis. The final section of the chapter presents biclustering applications.
