Cellular apoptosis susceptibility protein (CAS) suppresses the proliferation of breast cancer cells by upregulated cyp24a1.
TL;DR: Observations clarified the previous conflicting results on the cell fates of the breast cells regulated by CAS and provide new insight into the role of CAS in the development of breast cancer.
Abstract: Breast cancer is the most common cancer in women. Although several studies demonstrated cellular apoptosis susceptibility protein (CAS) involved in the development of breast cancer, the underlying mechanisms of CAS regulating cell processes in the breast cancer remain elusive. In the present study, we explored the possible mechanism of CAS in contributing to the cell proliferation in the breast cancer cell line MCF-7. Knockdown of CAS led to the reduction of cell viability and proliferation. Furthermore, cell cycle was arrested in G0/G1 phase after knocking down CAS with the decrease of cyclinD1. In addition, RNA-seq analysis for the CAS knockdown cells demonstrated that total eleven genes were significantly altered (Fold changes > 2). Of note, the expression of cyp24a1 was dramatically increased in the shCAS cells compared to that of shNC cells as well as confirmed by quantitative real-time polymerase chain reaction (qPCR). These observations clarified the previous conflicting results on the cell fates of the breast cells regulated by CAS and provide new insight into the role of CAS in the development of breast cancer.
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TL;DR: Wang et al. as mentioned in this paper proposed a new algorithm Crystall to find the methylomic features for this regression model, and achieved the mean absolute error (MAE) of about 1 month for predicting how long a breast cancer patient will live within five years.
Abstract: Breast cancer is one of main causes of death for women. Most of the existing survival analyses focus on the features’ associations with whether the patients may survive five years or not. The personalized question remains largely unresolved about how long a breast cancer patient will live. This study aims to predict the patient-specific survival time of breast cancer patients. It formulates the personalized question into two machine learning problems. The first problem is the binary classification of whether a patient will live longer than five years or not. The second one is to build a regression model to predict the patient’s survival time within five years. The methylome of a breast cancer patient is used for the prediction. A new algorithm Crystall is presented to find the methylomic features for this regression model. Our models perform well in the above two problems, and achieve the mean absolute error (MAE) of about 1 month for predicting how long a breast cancer patient will live within five years. The detected biomarker genes demonstrate close connections with breast cancers.
5 citations
TL;DR: Butyrate-producing microbes, such as B. pullicaecorum, may reverse the genetic distortion caused by p53 mutations in CRC by regulating CSE1L expression levels, indicating that butyrate can impair CSE 1L-induced tumorigenic potential.
Abstract: The chromosome segregation 1-like (CSE1L) protein, which regulates cellular mitosis and apoptosis, was previously found to be overexpressed in colorectal cancer (CRC) cells harboring mutations. Therefore, regulating CSE1L expression may confer chemotherapeutic effects against CRC. The gut microflora can regulate gene expression in colonic cells. In particular, metabolites produced by the gut microflora, including the short-chain fatty acid butyrate, have been shown to reduce CRC risk. Butyrates may exert antioncogenic potential in CRC cells by modulating p53 expression. The present study evaluated the association between CSE1L expression and butyrate treatment from two non-transformed colon cell lines (CCD-18Co and FHC) and six CRC cell lines (LS 174T, HCT116 p53+/+, HCT116 p53−/−, Caco-2, SW480 and SW620). Lentiviral knockdown of CSE1L and p53, reverse transcription-quantitative PCR (CSE1L, c-Myc and p53), western blotting [CSE1L, p53, cyclin (CCN) A2, CCNB2 and CCND1], wound healing assay (cell migration), flow cytometry (cell cycle analysis) and immunofluorescence staining (CSE1L and tubulin) were adopted to verify the effects of butyrate on CSE1L-expressing CRC cells. The butyrate-producing gut bacteria Butyricicoccus pullicaecorum was administered to mice with 1,2-dimethylhydrazine-induced colon tumors before the measurement of CSE1L expression. The effects of B. pullicaecorum on CSE1L expression were then assessed by immunohistochemical staining for CSE1L and p53 in tissues from CRC-bearing mice. Non-cancerous colon cells with the R273H p53 mutation or CRC cells haboring p53 mutations were found to exhibit significantly higher CSE1L expression levels. CSE1L knockdown in HCT116 p53−/− cells resulted in G1-and G2/M-phase cell cycle arrest. Furthermore, in HCT116 p53−/− cells, CSE1L expression was already high at interphase, increased at prophase, peaked during metaphase before declining at cytokinesis but remained relatively high compared with that in HCT116 expressing wild-type p53. Significantly decreased expression levels of CSE1L were also observed in HCT116 p53−/− cells that were treated with butyrate for 24 h. In addition, the migration of HCT116 p53−/− cells was significantly decreased after CSE1L knockdown or butyrate treatment. Tumors with more intense nuclear p53 staining and weaker CSE1L staining were found in mice bearing DMH/DSS-induced CRC that were administered with B. pullicaecorum. Taken together, the results indicated that butyrate can impair CSE1L-induced tumorigenic potential. In conclusion, butyrate-producing microbes, such as B. pullicaecorum, may reverse the genetic distortion caused by p53 mutations in CRC by regulating CSE1L expression levels.
3 citations
TL;DR: It is shown that the expression of CAS was higher in TNBC samples than in non‐TNBC samples in the Gene Expression Omnibus database, and complement pathway activity was significantly elevated and complement component 3 (C3) was significantly upregulated.
Abstract: Triple‐negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. However, the treatment regimens for TNBC are limited. Chromosome segregation 1‐like (CSE1L), also called cellular apoptosis susceptibility protein (CAS), is highly expressed in breast cancer and plays a crucial role in the progression of various tumours. However, the involvement of CAS in TNBC remains elusive. In this study, we showed that the expression of CAS was higher in TNBC samples than in non‐TNBC samples in the Gene Expression Omnibus database. Knockdown of CAS inhibited MDA‐MB‐231 cell growth, migration and invasion. Further RNA‐seq analysis revealed that complement pathway activity was significantly elevated. Of note, complement component 3 (C3), the key molecule in the complement pathway, was significantly upregulated, and the expression of C3 was negatively correlated with that of CAS in breast cancer. Lower C3 expression was related to poor prognosis. Interestingly, the expression level of C3 was positively correlated with the infiltration of multiple immune cells. Taken together, our findings suggest that CAS participates in the development of TNBC through C3‐mediated immune cell suppression and might constitute a potential therapeutic target for TNBC.
2 citations
TL;DR: The cellular apoptosis susceptibility gene (CAS) has been found to play crucial roles in cell proliferation/apoptosis and progression of various cancers as mentioned in this paper , and it has been shown that CAS may exert its effect on spermatogenesis.
Abstract: The cellular apoptosis susceptibility gene (CAS) (also named chro-mosomal segregation 1 like [CSE1L] and exportin-2) has been found to play crucial roles in cell proliferation/apoptosis and progression of various cancers. While the functions of CAS in reproduction have not been well understood, previous studies in human trophoblast cells and seminoma showed that CAS is involved in cell proliferation and mitosis. 1,2 Our previous study demonstrated the expression of CAS in human testis and testicular cancers. 3 We, therefore, speculate that CAS may exert its effect on spermatogenesis. The process of spermatogenesis is complicated and tightly regulated. The sequential differentiating germ cells were produced during the mouse testicular development. Undifferentiated spermatogonia begin to differentiate 4 days postpartum (dpp). The spermatocytes come up in 10dpp, and the meiosis accom-plished at 20 – 21dpp. The spermatozoa was 35dpp testis. The mice are sexual maturation at 56dpp. Therefore, we collected several key time points of the spermatogonia, spermatocytes, round spermatids, and spermatozoa illustrate expression profile of CAS during mouse testicular qPCR results CAS mRNA at all 7 56 days postpartum (dpp)
1 citations
01 Jan 2022
TL;DR: In this paper , the authors reviewed the application of proteomics to primary bone cancers and bone metastatic cancers, identifying potential novel drug targets as well as proteins which can inform patient treatment decisions as potential prognostic and/or diagnostic biomarkers.
Abstract: Bone-resident primary cancers and bone metastatic cancers are major causes of patient morbidity, mortality, and reduced quality of life. The presence of cancer cells within bone causes significant alterations in normal bone homeostasis and results in an increased rate of fractures and other skeletal-related events (SREs). Alterations in cell signaling within both the tumor cells themselves and the surrounding bone microenvironment are potential drug targets for treatment of this important disease development. As proteins are the key functional molecules within cells, proteomics has considerable potential to identify the mechanistic drivers which alter during the process of cancer development within bone, and cancer metastasis to bone. This chapter reviews the application of proteomics to primary bone cancers and bone metastatic cancers, identifying potential novel drug targets as well as proteins which can inform patient treatment decisions as potential prognostic and/or diagnostic biomarkers. Future applications of proteomic methods to bone cancers are also described.
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TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
Abstract: This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions There will be an estimated 181 million new cancer cases (170 million excluding nonmelanoma skin cancer) and 96 million cancer deaths (95 million excluding nonmelanoma skin cancer) in 2018 In both sexes combined, lung cancer is the most commonly diagnosed cancer (116% of the total cases) and the leading cause of cancer death (184% of the total cancer deaths), closely followed by female breast cancer (116%), prostate cancer (71%), and colorectal cancer (61%) for incidence and colorectal cancer (92%), stomach cancer (82%), and liver cancer (82%) for mortality Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality) Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts CA: A Cancer Journal for Clinicians 2018;0:1-31 © 2018 American Cancer Society
58,675 citations
"Cellular apoptosis susceptibility p..." refers background in this paper
...Breast cancer is the most common cancer and the leading cause of mortality among women [1]....
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TL;DR: It is found that intraflagellar transport 20 mediates the ability of Ror2 signaling to induce the invasiveness of tumors that lack primary cilia, and IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex.
Abstract: Signaling through the Ror2 receptor tyrosine kinase promotes invadopodia formation for tumor invasion. Here, we identify intraflagellar transport 20 (IFT20) as a new target of this signaling in tumors that lack primary cilia, and find that IFT20 mediates the ability of Ror2 signaling to induce the invasiveness of these tumors. We also find that IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex, which promotes Golgi ribbon formation in achieving polarized secretion for cell migration and invasion. Furthermore, IFT20 promotes the efficiency of transport through the Golgi complex. These findings shed new insights into how Ror2 signaling promotes tumor invasiveness, and also advance the understanding of how Golgi structure and transport can be regulated.
13,354 citations
TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
5,980 citations
"Cellular apoptosis susceptibility p..." refers methods in this paper
...The online databases GEPIA (https ://gepia .cance r-pku. cn/), Kaplan Meiper (KM) plotter (https ://kmplo t.com/ analy sis/) and UALCAN (https ://ualca n.path.uab.edu/index .html) were used for assessing the expression comparison and survival correlation of individuals with breast cancer....
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...In the present study, we have found the elevated expression of CAS in 1085 women with breast cancer in the GEPIA analysis....
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...With this question, we explored GEPIA [32], Kaplan Meier plotter [33–35] and UALCAN [36] database to analyze the correlation between the CAS expression and breast cancer prognosis....
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...For GEPIA analysis, breast cancer patients in the database were included from The Cancer Genome Atlas (TCGA) and Genotype-tissue Expression (GTEx)....
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...1 The expression and prognostic effect of CAS in breast cancer. a Comparison of CAS expression in breast cancer (n = 1085) and normal tissue (n = 291) using GEPIA tools [red: tumor (n = 1085); black: normal tissue (n = 291)]. b The prognostic plot for the overall survival of breast cancer patients with high (red line, n = 879) and low (black line, n = 523) CAS mRNA expression. c The prognostic plot for the overall survival of breast cancer patients with high (red line, n = 879) and low (black line, n = 523) CAS protein expression level [red: patients with high expression of CAS protein (n = 28); black: patients with low expression of CAS protein (n = 37)] Medical Oncology (2020) 37:43 1 3 Page 5 of 11 43 high expressed CAS (n = 879) patients (Fig....
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TL;DR: UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data, serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers.
3,546 citations
"Cellular apoptosis susceptibility p..." refers methods in this paper
...UALCAN was used to obtain Kaplan–Meier survival plots of CYP24A1 in breast cancer....
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...With this question, we explored GEPIA [32], Kaplan Meier plotter [33–35] and UALCAN [36] database to analyze the correlation between the CAS expression and breast cancer prognosis....
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...The online databases GEPIA (https ://gepia .cance r-pku. cn/), Kaplan Meiper (KM) plotter (https ://kmplo t.com/ analy sis/) and UALCAN (https ://ualca n.path.uab.edu/index .html) were used for assessing the expression comparison and survival correlation of individuals with breast cancer....
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...TCGA and MET500 datasets compose the UALCAN database which is used to evaluate the expression of CAS in different stages and different subtypes of breast cancer....
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TL;DR: An online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients, and which validated the capability of microarrays to determine estrogen receptor status in 1,231 patients.
Abstract: Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.
2,395 citations