4.6
CiteScore
2.2
Impact Factor
- ISSN 1674-8301
- CN 32-1810/R
4.6
2.2
| Citation: |
Xiao Shi, Xinxin Si, Ershao Zhang, Ruochen Zang, Nan Yang, He Cheng, Zhihong Zhang, Beijing Pan, Yujie Sun. Paclitaxel-induced stress granules increase LINE-1 mRNA stability to promote drug resistance in breast cancer cells[J]. The Journal of Biomedical Research, 2021, 35(6): 411-424. DOI: 10.7555/JBR.35.20210105
|
"Human genome epidemiology (HuGE)" was first proposed in 1998 by Khoury and Doman, and defined as an evolving field that uses epidemiologic methods and approaches in population-based studies to assess the impact of human genetic variations on health and diseases[1]. HuGE has been viewed as the intersection between molecular epidemiology and genetic epidemiology, which aims to translate human genetic research findings into meaningful actions to improve health and prevent disease[2]. In the past two decades, a large and rapidly increasing number of studies on HuGE have been carried out, along with the great progress in genomics technologies. These studies not only greatly promote people's understanding of the influence of genetic variations on disease occurrence, but also provide important theoretical basis for personalized healthcare and disease prevention. However, as a new and developing research field, it also faces some difficulties and challenges. Here, we attempt to briefly describe the current progress, opportunities and challenges of HuGE, which will help medical and public health professionals integrate findings of genomics into practice to improve the health of individuals and populations.
In the recent 10 to 15 years, perhaps the most important development of HuGE has been the emergence of genome-wide association studies (GWAS)[3], which tests hundreds of thousands to millions of genetic variants across the genomes to identify genotype-phenotype associations[4]. Since the first GWAS for age-related macular degeneration was published in 2005[5], more than 50 000 genetic loci related to complex diseases or traits have been identified[6]. These findings have advanced our current knowledge in genetic architecture of complex trait or disease (identification of novel susceptibility genes and biological mechanisms) and promoted the practice in clinical care (discovery of disease progress biomarkers and new targets for therapeutic interventions) and personalized medicine (risk prediction and optimization of therapies)[4]. For example, recent studies have shown that polygenic risk score (PRS) can quantify the cumulative effect of genetic variants discovered by GWAS to identify high-risk individuals, thereby improving disease screening or clinical outcomes through early detection, prevention or treatment[7]. In 2019, our research team first built a PRS based on 19 lung cancer susceptibility loci in Chinese populations and evaluated the utility and effectiveness of the generated PRS in predicting subpopulations at high risk of lung cancer in a large-scale prospective cohort[8]. The results showed that GWAS-derived PRS can be effectively used in discriminating subpopulations at high risk of lung cancer, who might benefit from lung cancer screening for precision prevention in Chinese populations[8]. Similar findings have been reported in cardiovascular diseases, diabetes, inflammatory bowel disease, other types of cancers, etc.[9]. In addition, the discovery of genetic variations based on GWAS has been used to identify several novel candidate drugs that are now being applied in clinics or evaluated in clinical trials[5]. A recent study also reported that patient-specific human leukocyte antigen class I genotype could influence the efficacy of immune checkpoint inhibitors targeting cytotoxic T-lymphocyte-associated protein 4 and programmed cell death protein 1/programmed cell death 1 ligand 1 in cancer patients, suggesting an important role of genetic variations in immunotherapy[10]. Thus, the Clinical Pharmacogenetics Implementation Consortium has developed a rigorous approach to evaluate the clinical value and interpretation of genetic variants associated with drug response, providing clinical decision supports for physicians. However, despite the great success in identifying disease loci, GWAS still has some limitations, such as limited sample size, "missing" heritability and interpretation of GWAS associations[4]. Substantial efforts have been made to overcome these difficulties. First, larger sample size has been the most effective way of increasing the power of GWAS to detect the small or moderately sized effects. Studies have indicated that many common variants with relatively weak effects on human disease were missed by GWAS due to a lack of statistical power[11]. Now the global multi-center data integration and collaboration for GWAS become the trend and the research subjects can reach more than 100 000 people to identify all causal genetic variants and measure how much trait variation they explain. Secondly, rare variants may make a major contribution to missing heritability, which are not captured by common SNPs on current genotyping arrays[12]. Thus, the research scope has expanded to low-frequency or rare genetic variations by using exome or whole-genome sequencing studies[13]. These sequencing findings would also improve the application value of PRS. However, very large samples will be needed for rare variants in such studies unless the effect size of the variant is particularly large. Additionally, it is a great challenge to identify causal genes or SNPs although GWAS have identified thousands of variants associated with common diseases and complex traits. Remarkably, with the promotion of large-scale resource-based projects such as ENCODE, TCGA, and GTEx, the biological significance of genetic loci in the development of human diseases is gradually uncovered[14].
It has been widely accepted that both genetic variations and environmental exposures affect disease risk, and individuals with different genotypes may respond differently to environmental exposures and generate an array of phenotypic landscape[15]. Such gene-environment (G×E) interactions may be responsible for a large fraction of the unexplained variances in heritability and disease risk[14]. In the meantime, the study of G×E interaction is important for better understanding the biological pathways, estimating population-attributable risk(s), and identifying individuals who may be more susceptible to diseases[16]. In the past, G×E interactions have been investigated for a wide range of candidate genes and exposures for many complex traits. Limited by factors such as small sample size, representativeness of genes or loci and improper correction of multiple comparisons, these studies only provide little evidence[17]. At present, progress of the large-scale genomic studies makes it possible to explore the G×E interactions through more dense panels of genetic variants and larger sample sizes, and even detect interactions with small effect sizes, rare frequencies, and higher order interactions[18]. In 2012, the National Institutes of Health had launched the Genetic Associations and Mechanisms in Oncology (GAME-ON) Initiative for five common malignant tumors, including breast, prostate, ovarian, lung, and colorectal cancers. It aimed to "rapidly move forward promising leads from initial cancer GWAS by….unraveling the function of genetic variants and how environmental factors may influence the genetic effect…"[19]. Since then, studies focusing on G×E interactions have begun to yield interesting findings. For example, our research team first identified several novel loci that were significantly associated with lung cancer risk in the Chinese Han population, including 13q12.12, 22q12.2, 1p36.32, and 5q31.1[20–21]. Among those, a well-replicated GWAS risk SNP rs753955 within 13q12.12 is situated in the gene desert region, about 150 kb away from the nearest upstream gene, TNFRSF19. When compared to non-smokers with wild genotype of this variant, the lung cancer risk increased to 3.8 times for those smokers with variant genotypes, suggesting a potential gene-smoking interaction[20]. Functional assays indicated that this susceptible locus in 13q12.12 could decrease the expression of TNFRSF19 and promote the malignant transformation of lung epithelial cells caused by NNK[22]. These results revealed the potential biological mechanism underlying the interaction between susceptible genes and tobacco carcinogens. It should be noted that bias can be induced in case-control studies of genotype effects if the underlying population is genetically stratified or admixed. Investigators have extended family-based studies to G×E interaction studies, which can provide strategies for testing genetic effects that are robust to undetected/unaccounted for population substructure[23]. The Framingham Heart Study is a well-known example of a family-based study. However, challenges still exist in G×E interaction studies: the complexity of measuring environment exposures, limited range of genetic and/or environmental variation, limitation of different statistical methods for interaction analysis, and lack of data on the biological significance of most genetic variants[18]. More importantly, the risk of complex disease is a consequence of multiple genes in multiple biologic pathways interacting with each other and with cumulative environmental factors over a lifetime. It will require new paradigms for interdisciplinary collaborative research with very large-scale studies, as well as new analysis tools to help scientists reveal the complex multi-gene-environment interactions involved in human diseases.
Despite some successes at identifying genetic and environmental risk factors for complex diseases, they still represent only the tip of the iceberg and much of the etiology remains unexplained. This may be due in part to the limitation of many studies on a single or small set of risk factors or data types. With the availability of high throughput -omics technologies, researchers gradually realize that a more comprehensive and systematic analysis with multiple dimensions, integration of genomics, transcriptomic, metabolomic, and other omics data, is needed to better understand their contributions to diseases at multiple levels as well as their interactions. Therefore, "systems epidemiology" emerged as a new research discipline that integrates multi-omics together with epidemiological data to create a systems network that can be used to better characterize the diverse range of factors influencing disease development[24–25]. Remarkably, systems epidemiology involves not only the measurement of biomarkers, such as genomic, transcriptomic, proteomic, and metabolomic profiles, but also a variety of environmental interaction components including smoking, behavior, sociodemographic factors, and group levels that may affect health and disease[26]. Some researchers have proposed a globolomic study design for systems epidemiology, which will collect biological samples at the beginning of the follow-up and the time at diagnosis based on large-scale prospective cohort studies to detect DNA, RNA and other biomarkers, and then combine with the outcomes of the cohort study to evaluate complex interactions between multiple exposures and their dynamics encompassing human diseases[24]. However, the most compelling challenge will be to integrate multi-level data. Recently, some statistical methods have been developed to integrate different types of data, such as TCGA analysis platform which has comprehensively used the multi-dimensional genomic data (including variations, copy numbers, epigenetic data, gene expression, and miRNA sequencing data) of more than 30 kinds of tumors to mine gene networks related to cancers[27–28]. Although such analysis does not provide a complete understanding of the human system, the findings make us more deeply aware of the pathogenesis of disease.
As described above, new knowledge and technology have given genome epidemiology the possibility of a new research discipline, which could form an important scientific foundation for using genetic information to improve health and prevent disease. However, the development in genome epidemiology still requires continued technological advances in high-throughput methods, enhanced bioinformatics and analytical tools, coordinated efforts that span multiple disciplines of laboratory sciences, medicine and public health. In addition, well-designed prospective cohort studies with large sample size, long-term follow-up, availability of archived biological samples, and detailed measures of exposures, are also necessary for the successful application of genome epidemiology into medicine and public health. Finally, it is difficult to appropriately evaluate the utility of genetic information based solely on measures such as relative risks of genetic loci and PRS. Other factors such as environmental and lifestyle factors, also contribute to the risk prediction models. Moreover, clinicians and scientists need to engage with the public and get across the fact that, for many complex diseases, metrics like PRS are probabilistic at the population level. Although a person's genetic makeup cannot be altered, some lifestyle and environmental modifications may reduce disease risk in people with a genetic predisposition.
This work was supported by the National Natural Science Foundation of China (Grant No. 81521004 and No. 81922061).
| [1] |
Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer[J]. N Engl J Med, 2010, 363(20): 1938–1948. doi: 10.1056/NEJMra1001389
|
| [2] |
Liedtke C, Mazouni C, Hess KR, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer[J]. J Clin Oncol, 2008, 26(8): 1275–1281. doi: 10.1200/JCO.2007.14.4147
|
| [3] |
Burns KH. Transposable elements in cancer[J]. Nat Rev Cancer, 2017, 17(7): 415–424. doi: 10.1038/nrc.2017.35
|
| [4] |
Kazazian HH Jr, Goodier JL. LINE drive: retrotransposition and genome instability[J]. Cell, 2002, 110(3): 277–280. doi: 10.1016/S0092-8674(02)00868-1
|
| [5] |
Schwertz H, Rowley JW, Schumann GG, et al. Endogenous LINE-1 (long interspersed nuclear element-1) reverse transcriptase activity in platelets controls translational events through RNA-DNA hybrids[J]. Arterioscler Thromb Vasc Biol, 2018, 38(4): 801–815. doi: 10.1161/ATVBAHA.117.310552
|
| [6] |
Dueva R, Akopyan K, Pederiva C, et al. Neutralization of the positive charges on histone tails by RNA promotes an open chromatin structure[J]. Cell Chem Biol, 2019, 26(10): 1436–1449.e5. doi: 10.1016/j.chembiol.2019.08.002
|
| [7] |
Cruickshanks HA, Vafadar-Isfahani N, Dunican DS, et al. Expression of a large LINE-1-driven antisense RNA is linked to epigenetic silencing of the metastasis suppressor gene TFPI-2 in cancer[J]. Nucleic Acids Res, 2013, 41(14): 6857–6869. doi: 10.1093/nar/gkt438
|
| [8] |
Lavasanifar A, Sharp CN, Korte EA, et al. Long interspersed nuclear element-1 mobilization as a target in cancer diagnostics, prognostics and therapeutics[J]. Clin Chim Acta, 2019, 493: 52–62. doi: 10.1016/j.cca.2019.02.015
|
| [9] |
Yang Q, Feng F, Zhang F, et al. LINE-1 ORF-1p functions as a novel HGF/ETS-1 signaling pathway co-activator and promotes the growth of MDA-MB-231 cell[J]. Cell Signal, 2013, 25(12): 2652–2660. doi: 10.1016/j.cellsig.2013.08.029
|
| [10] |
Feng F, Lu Y, Zhang F, et al. Long interspersed nuclear element ORF-1 protein promotes proliferation and resistance to chemotherapy in hepatocellular carcinoma[J]. World J Gastroenterol, 2013, 19(7): 1068–1078. doi: 10.3748/wjg.v19.i7.1068
|
| [11] |
Farkash EA, Kao GD, Horman SR, et al. Gamma radiation increases endonuclease-dependent L1 retrotransposition in a cultured cell assay[J]. Nucleic Acids Res, 2006, 34(4): 1196–1204. doi: 10.1093/nar/gkj522
|
| [12] |
Okudaira N, Okamura T, Tamura M, et al. Long interspersed element-1 is differentially regulated by food-borne carcinogens via the aryl hydrocarbon receptor[J]. Oncogene, 2013, 32(41): 4903–4912. doi: 10.1038/onc.2012.516
|
| [13] |
Guler GD, Tindell CA, Pitti R, et al. Repression of stress-induced LINE-1 expression protects cancer cell subpopulations from lethal drug exposure[J]. Cancer Cell, 2017, 32(2): 221–237.e13. doi: 10.1016/j.ccell.2017.07.002
|
| [14] |
Mahboubi H, Stochaj U. Cytoplasmic stress granules: dynamic modulators of cell signaling and disease[J]. Biochim Biophys Acta Mol Basis Dis, 2017, 1863(4): 884–895. doi: 10.1016/j.bbadis.2016.12.022
|
| [15] |
Fujimura K, Sasaki AT, Anderson P. Selenite targets eIF4E-binding protein-1 to inhibit translation initiation and induce the assembly of non-canonical stress granules[J]. Nucleic Acids Res, 2012, 40(16): 8099–8110. doi: 10.1093/nar/gks566
|
| [16] |
Liu W, Huang CY, Lu IC, et al. Inhibition of glioma growth by minocycline is mediated through endoplasmic reticulum stress-induced apoptosis and autophagic cell death[J]. Neuro Oncol, 2013, 15(9): 1127–1141. doi: 10.1093/neuonc/not073
|
| [17] |
Vilas-Boas FDAS, da Silva AM, de Sousa LP, et al. Impairment of stress granule assembly via inhibition of the eIF2alpha phosphorylation sensitizes glioma cells to chemotherapeutic agents[J]. J Neurooncol, 2016, 127(2): 253–260. doi: 10.1007/s11060-015-2043-3
|
| [18] |
Arimoto K, Fukuda H, Imajoh-Ohmi S, et al. Formation of stress granules inhibits apoptosis by suppressing stress-responsive MAPK pathways[J]. Nat Cell Biol, 2008, 10(11): 1324–1332. doi: 10.1038/ncb1791
|
| [19] |
Thedieck K, Holzwarth B, Prentzell MT, et al. Inhibition of mTORC1 by astrin and stress granules prevents apoptosis in cancer cells[J]. Cell, 2013, 154(4): 859–874. doi: 10.1016/j.cell.2013.07.031
|
| [20] |
Gareau C, Fournier MJ, Filion C, et al. p21WAF1/CIP1 upregulation through the stress granule-associated protein CUGBP1 confers resistance to bortezomib-mediated apoptosis[J]. PLoS One, 2011, 6(5): e20254. doi: 10.1371/journal.pone.0020254
|
| [21] |
Doucet AJ, Hulme AE, Sahinovic E, et al. Characterization of LINE-1 ribonucleoprotein particles[J]. PLoS Genet, 2010, 6(10): e1001150. doi: 10.1371/journal.pgen.1001150
|
| [22] |
Khong A, Matheny T, Jain S, et al. The stress granule transcriptome reveals principles of mRNA accumulation in stress granules[J]. Mol Cell, 2017, 68(4): 808–820.e5. doi: 10.1016/j.molcel.2017.10.015
|
| [23] |
Ying W, Wang S, Shi J, et al. ER-/ER+ breast cancer cell lines exhibited different resistance to paclitaxel through pulse selection[J]. Med Oncol, 2012, 29(2): 495–502. doi: 10.1007/s12032-011-9889-9
|
| [24] |
Bojang P Jr, Roberts RA, Anderton MJ, et al. Reprogramming of the HepG2 genome by long interspersed nuclear element-1[J]. Mol Oncol, 2013, 7(4): 812–825. doi: 10.1016/j.molonc.2013.04.003
|
| [25] |
Sciamanna I, Landriscina M, Pittoggi C, et al. Inhibition of endogenous reverse transcriptase antagonizes human tumor growth[J]. Oncogene, 2005, 24(24): 3923–3931. doi: 10.1038/sj.onc.1208562
|
| [26] |
Györffy B, Lanczky A, Eklund AC, et al. An online survival analysis tool to rapidly assess the effect of 22, 277 genes on breast cancer prognosis using microarray data of 1, 809 patients[J]. Breast Cancer Res Treat, 2010, 123(3): 725–731. doi: 10.1007/s10549-009-0674-9
|
| [27] |
Agostini F, Zanzoni A, Klus P, et al. catRAPID omics: a web server for large-scale prediction of protein-RNA interactions[J]. Bioinformatics, 2013, 29(22): 2928–2930. doi: 10.1093/bioinformatics/btt495
|
| [28] |
Muppirala UK, Honavar VG, Dobbs D. Predicting RNA-protein interactions using only sequence information[J]. BMC Bioinformatics, 2011, 12: 489. doi: 10.1186/1471-2105-12-489
|
| [29] |
Kale SP, Moore L, Deininger PL, et al. Heavy metals stimulate human LINE-1 retrotransposition[J]. Int J Environ Res Public Health, 2005, 2(1): 14–23. doi: 10.3390/ijerph2005010014
|
| [30] |
Teneng I, Stribinskis V, Ramos KS. Context-specific regulation of LINE-1[J]. Genes Cells, 2007, 12(10): 1101–1110. doi: 10.1111/j.1365-2443.2007.01117.x
|
| [31] |
Whongsiri P, Pimratana C, Wijitsettakul U, et al. LINE-1 ORF1 protein is Up-regulated by reactive oxygen species and associated with bladder urothelial carcinoma progression[J]. Cancer Genomics Proteomics, 2018, 15(2): 143–151. doi: 10.21873/cgp.20072
|
| [32] |
Lim J, Ha M, Chang H, et al. Uridylation by TUT4 and TUT7 marks mRNA for degradation[J]. Cell, 2014, 159(6): 1365–1376. doi: 10.1016/j.cell.2014.10.055
|
| [33] |
Hamdorf M, Idica A, Zisoulis DG, et al. miR-128 represses L1 retrotransposition by binding directly to L1 RNA[J]. Nat Struct Mol Biol, 2015, 22(10): 824–831. doi: 10.1038/nsmb.3090
|
| [34] |
Fung L, Guzman H, Sevrioukov E, et al. miR-128 restriction of LINE-1 (L1) retrotransposition is dependent on targeting hnRNPA1 mRNA[J]. Int J Mol Sci, 2019, 20(8): 1955. doi: 10.3390/ijms20081955
|
| [35] |
Kawakami A, Tian Q, Duan X, et al. Identification and functional characterization of a TIA-1-related nucleolysin[J]. Proc Natl Acad Sci USA, 1992, 89(18): 8681–8685. doi: 10.1073/pnas.89.18.8681
|
| [36] |
Meyer C, Garzia A, Mazzola M, et al. The TIA1 RNA-binding protein family regulates EIF2AK2-mediated stress response and cell cycle progression[J]. Mol Cell, 2018, 69(4): 622–635.e6. doi: 10.1016/j.molcel.2018.01.011
|
| [37] |
Bley N, Lederer M, Pfalz B, et al. Stress granules are dispensable for mRNA stabilization during cellular stress[J]. Nucleic Acids Res, 2015, 43(4): e26. doi: 10.1093/nar/gku1275
|
| [38] |
Goodier JL, Zhang L, Vetter MR, et al. LINE-1 ORF1 protein localizes in stress granules with other RNA-binding proteins, including components of RNA interference RNA-induced silencing complex[J]. Mol Cell Biol, 2007, 27(18): 6469–6483. doi: 10.1128/MCB.00332-07
|
| [39] |
Song MS, Grabocka E. Stress granules in cancer[M]// Pedersen SHF. Reviews of physiology, biochemistry and pharmacology. Berlin Heidelberg: Springer, 2020: 1–28.
|
| [40] |
Anderson P, Kedersha N, Ivanov P. Stress granules, P-bodies and cancer[J]. Biochim Biophys Acta, 2015, 1849(7): 861–870. doi: 10.1016/j.bbagrm.2014.11.009
|
| [41] |
Sahakyan AB, Murat P, Mayer C, et al. G-quadruplex structures within the 3′UTR of LINE-1 elements stimulate retrotransposition[J]. Nat Struct Mol Biol, 2017, 24(3): 243–247. doi: 10.1038/nsmb.3367
|
| [42] |
Gellert M, Lipsett MN, Davies DR. Helix formation by guanylic acid[J]. Proc Natl Acad Sci U S A, 1962, 48(12): 2013–2018. doi: 10.1073/pnas.48.12.2013
|
| [43] |
Kato M, Han TW, Xie S, et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels[J]. Cell, 2012, 149(4): 753–767. doi: 10.1016/j.cell.2012.04.017
|
| [44] |
Van Treeck B, Protter DSW, Matheny T, et al. RNA self-assembly contributes to stress granule formation and defining the stress granule transcriptome[J]. Proc Natl Acad Sci U S A, 2018, 115(11): 2734–2739. doi: 10.1073/pnas.1800038115
|
| [45] |
Ivanov P, O'Day E, Emara MM, et al. G-quadruplex structures contribute to the neuroprotective effects of angiogenin-induced tRNA fragments[J]. Proc Natl Acad Sci U S A, 2014, 111(51): 18201–18206. doi: 10.1073/pnas.1407361111
|
| [46] |
Dai L, Huang Q, Boeke JD. Effect of reverse transcriptase inhibitors on LINE-1 and Ty1 reverse transcriptase activities and on LINE-1 retrotransposition[J]. BMC Biochem, 2011, 12: 18. doi: 10.1186/1471-2091-12-18
|
| [47] |
Hecht M, Erber S, Harrer T, et al. Efavirenz has the highest anti-proliferative effect of non-nucleoside reverse transcriptase inhibitors against pancreatic cancer cells[J]. PLoS One, 2015, 10(6): e0130277. doi: 10.1371/journal.pone.0130277
|
| [48] |
Houédé N, Pulido M, Mourey L, et al. A phase II trial evaluating the efficacy and safety of efavirenz in metastatic castration-resistant prostate cancer[J]. Oncologist, 2014, 19(12): 1227–1228. doi: 10.1634/theoncologist.2014-0345
|
| [49] |
Buchan JR, Kolaitis RM, Taylor JP, et al. Eukaryotic stress granules are cleared by autophagy and Cdc48/VCP function[J]. Cell, 2013, 153(7): 1461–1474. doi: 10.1016/j.cell.2013.05.037
|
| [50] |
Bellisai C, Sciamanna I, Rovella P, et al. Reverse transcriptase inhibitors promote the remodelling of nuclear architecture and induce autophagy in prostate cancer cells[J]. Cancer Lett, 2020, 478: 133–145. doi: 10.1016/j.canlet.2020.02.029
|
| [51] |
Purnell PR, Fox HS. Efavirenz induces neuronal autophagy and mitochondrial alterations[J]. J Pharmacol Exp Ther, 2014, 351(2): 250–258. doi: 10.1124/jpet.114.217869
|
| [52] |
Apostolova N, Gomez-Sucerquia LJ, Gortat A, et al. Compromising mitochondrial function with the antiretroviral drug efavirenz induces cell survival-promoting autophagy[J]. Hepatology, 2011, 54(3): 1009–1019. doi: 10.1002/hep.24459
|
| [53] |
Guo H, Chitiprolu M, Gagnon D, et al. Autophagy supports genomic stability by degrading retrotransposon RNA[J]. Nat Commun, 2014, 5: 5276. doi: 10.1038/ncomms6276
|
| [54] |
Gorrini C, Harris IS, Mak TW. Modulation of oxidative stress as an anticancer strategy[J]. Nat Rev Drug Discov, 2013, 12(12): 931–947. doi: 10.1038/nrd4002
|
| [55] |
Protter DSW, Parker R. Principles and properties of stress granules[J]. Trends Cell Biol, 2016, 26(9): 668–679. doi: 10.1016/j.tcb.2016.05.004
|
| [1] | Pavan Kumar Dhanyamraju. Drug resistance mechanisms in cancers: Execution of pro-survival strategies[J]. The Journal of Biomedical Research, 2024, 38(2): 95-121. DOI: 10.7555/JBR.37.20230248 |
| [2] | Yang Ying, Chen Xiang, Ma Changyan. Insulin receptor is implicated in triple-negative breast cancer by decreasing cell mobility[J]. The Journal of Biomedical Research, 2021, 35(3): 189-196. DOI: 10.7555/JBR.34.20200082 |
| [3] | Chen Fei, Li Yuancheng, Qin Na, Wang Fengliang, Du Jiangbo, Wang Cheng, Du Fangzhi, Jiang Tao, Jiang Yue, Dai Juncheng, Hu Zhibin, Lu Cheng, Shen Hongbing. RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer[J]. The Journal of Biomedical Research, 2020, 34(2): 129-138. DOI: 10.7555/JBR.34.20190111 |
| [4] | Shen Tian, Han Bo'ang, Leng Yan, Yan Sen, Shi Junfeng, Yue Shen, Cheng Steven Y. Sonic Hedgehog stimulates migration of MCF-7 breast cancer cells through Rac1[J]. The Journal of Biomedical Research, 2019, 33(5): 297-307. DOI: 10.7555/JBR.32.20180100 |
| [5] | Yu-tuan Wu, Xin Li, Lin-jie Lu, Lu Gan, Wei Dai, Yan-ling Shi, Vishnu Prasad Adhikari, Kai-nan Wu, Ling-quan Kong. Effect of neoadjuvant chemotherapy on the expression of hormone receptors and Ki67 in Chinese breast cancer patients: A retrospective study of 525 patients[J]. The Journal of Biomedical Research, 2018, 32(3): 191-197. DOI: 10.7555/JBR.32.20170059 |
| [6] | Xu Hu, Linfei Jiang, Chenhui Tang, Yuehong Ju, Li Jiu, Yongyue Wei, Li Guo, Yang Zhao. Association of three single nucleotide polymorphisms of ESR1 with breast cancer susceptibility: a meta-analysis[J]. The Journal of Biomedical Research, 2017, 31(3): 213-225. DOI: 10.7555/JBR.31.20160087 |
| [7] | Lintao Wang, Yanyan Peng, Kaikai Shi, Haixiao Wang, Jianlei Lu, Yanli Li, Changyan Ma. Osthole inhibits proliferation of human breast cancer cells by inducing cell cycle arrest and apoptosis[J]. The Journal of Biomedical Research, 2015, 29(2): 132-138. DOI: 10.7555/JBR.27.20120115 |
| [8] | Xiaojian Ni, Yingchun Zhao, Jingjing Ma, Tiansong Xia, Xiaoan Liu, Qiang Ding, Xiaoming Zha, Shui Wang. Hypoxia-induced factor-1 alpha upregulates vascular endothelial growth factor C to promote lymphangiogenesis and angiogenesis in breast cancer patients[J]. The Journal of Biomedical Research, 2013, 27(6): 478-485. DOI: 10.7555/JBR.27.20130021 |
| [9] | Jianwei Zhao, Lin Liu, Anqing Zhang, Qin Chen, Wenxiang Fang, Lizhi Zeng, Jiachun Lu. Effect of EME1 exon variant Ile350Thr on risk and early onset of breast cancer in southern Chinese women[J]. The Journal of Biomedical Research, 2013, 27(3): 193-201. DOI: 10.7555/JBR.27.20130013 |
| [10] | Xiaoyan Wang, Guozhu Wang, Yi Zhao, Xiaoan Liu, Qiang Ding, Jingping Shi, Yin Ding, Shui Wang. STAT3 mediates resistance of CD44+CD24-/low breast cancer stem cells to tamoxifen in vitro[J]. The Journal of Biomedical Research, 2012, 26(5): 325-335. DOI: 10.7555/JBR.26.20110050 |
| 1. | Jia Y, Jia R, Dai Z, et al. Stress granules in cancer: Adaptive dynamics and therapeutic implications. iScience, 2024, 27(8): 110359. DOI:10.1016/j.isci.2024.110359 |
| 2. | Zhou H, Luo J, Mou K, et al. Stress granules: functions and mechanisms in cancer. Cell Biosci, 2023, 13(1): 86. DOI:10.1186/s13578-023-01030-6 |
| 3. | Chiang VS, DeRosa H, Park JH, et al. The Role of Transposable Elements in Sexual Development. Front Behav Neurosci, 2022, 16: 923732. DOI:10.3389/fnbeh.2022.923732 |
| 4. | Berrino E, Miglio U, Bellomo SE, et al. The Tumor-Specific Expression of L1 Retrotransposons Independently Correlates with Time to Relapse in Hormone-Negative Breast Cancer Patients. Cells, 2022, 11(12): 1944. DOI:10.3390/cells11121944 |
Figures(6) / Tables(2)
Supported by: Beijing Renhe Information Technology Co., Ltd. E-mail:
info@rhhz.net 
Authors and Reviewers
DownLoad: