Uncovering, role, LINE1, evolution, lung, adenocarcinoma
Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nat. Rev. Genet. 13, 795–806 (2012).
Google Scholar
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Google Scholar
Alexandrov, L. B. et al. Mutational signatures R. Science 354, 618–622 (2016).
Google Scholar
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Google Scholar
Yang, P., Wang, Y. & Macfarlan, T. S. The role of KRAB-ZFPs in transposable element repression and mammalian evolution. Trends Genet. 33, 871–881 (2017).
Google Scholar
Zhang, Y. et al. Global variations in lung cancer incidence by histological subtype in 2020: a population-based study. Lancet Oncol. 24, 1206–1218 (2023).
Google Scholar
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Google Scholar
Frankell, A. M. et al. The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature 616, 525–533 (2023).
Google Scholar
Burns, K. H. Transposable elements in cancer. Nat. Rev. Cancer 17, 415–424 (2017).
Google Scholar
Rodriguez-Martin, B. et al. Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition. Nat. Genet. 52, 306–319 (2020).
Google Scholar
Mendez-Dorantes, C. et al. Chromosomal rearrangements and instability caused by the LINE-1 retrotransposon. Preprint at bioRxiv https://doi.org/10.1101/2024.12.14.628481 (2024).
Rodić, N. et al. Retrotransposon insertions in the clonal evolution of pancreatic ductal adenocarcinoma. Nat. Med. 21, 1060–1064 (2015).
Google Scholar
Nguyen, T. H. M. et al. L1 retrotransposon heterogeneity in ovarian tumor cell evolution. Cell Rep. 23, 3730–3740 (2018).
Google Scholar
Zhang, T. et al. Genomic and evolutionary classification of lung cancer in never smokers. Nat. Genet. 53, 1348–1359 (2021).
Google Scholar
Landi, M. T. et al. Tracing lung cancer risk factors through mutational signatures in never-smokers. Am. J. Epidemiol. 190, 962–976 (2021).
Google Scholar
Zhu, B. et al. The genomic and epigenomic evolutionary history of papillary renal cell carcinomas. Nat. Commun. 11, 3096 (2020).
Google Scholar
Senkin, S. et al. Geographic variation of mutagenic exposures in kidney cancer genomes. Nature 629, 910–918 (2024).
Google Scholar
Díaz-Gay, M. et al. The mutagenic forces shaping the genomes of lung cancer in never smokers. Nature 644, 133–144 (2025).
Google Scholar
McGranahan, N. et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7, 283ra54 (2015).
Google Scholar
Otlu, B. et al. Topography of mutational signatures in human cancer. Cell Rep. 42, 112930 (2023).
Stamatoyannopoulos, J. A. et al. Human mutation rate associated with DNA replication timing. Nat. Genet. 41, 393–395 (2009).
Google Scholar
Whitfield, M. L., George, L. K., Grant, G. D. & Perou, C. M. Common markers of proliferation. Nat. Rev. Cancer 6, 99–106 (2006).
Google Scholar
Emami Nejad, A. et al. The role of hypoxia in the tumor microenvironment and development of cancer stem cell: a novel approach to developing treatment. Cancer Cell Int. 21, 62 (2021).
Google Scholar
Bhandari, V., Li, C. H., Bristow, R. G., Boutros, P. C. & PCAWG Consortium. Divergent mutational processes distinguish hypoxic and normoxic tumours. Nat. Commun. 11, 737 (2020).
Liu, N. et al. Selective silencing of euchromatic L1s revealed by genome-wide screens for L1 regulators. Nature 553, 228–232 (2018).
Google Scholar
Li, X. et al. LINE-1 transcription activates long-range gene expression. Nat. Genet. 56, 1494–1502 (2024).
Google Scholar
Scott, E. C. & Devine, S. E. The role of somatic L1 retrotransposition in human cancers. Viruses 9, 131 (2017).
Tubio, J. M. C. et al. Mobile DNA in cancer. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes. Science 345, 1251343 (2014).
Google Scholar
McKerrow, W. et al. LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint. Proc. Natl Acad. Sci. USA 119, e2115999119 (2022).
Kazazian, H. H. Jr & Moran, J. V. Mobile DNA in health and disease. N. Engl. J. Med. 377, 361–370 (2017).
Google Scholar
Petljak, M. et al. Characterizing mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis. Cell 176, 1282–1294 (2019).
Google Scholar
Nam, C. H. et al. Widespread somatic L1 retrotransposition in normal colorectal epithelium. Nature 617, 540–547 (2023).
Google Scholar
Levin, H. L. & Moran, J. V. Dynamic interactions between transposable elements and their hosts. Nat. Rev. Genet. 12, 615–627 (2011).
Google Scholar
Gasior, S. L., Wakeman, T. P., Xu, B. & Deininger, P. L. The human LINE-1 retrotransposon creates DNA double-strand breaks. J. Mol. Biol. 357, 1383–1393 (2006).
Google Scholar
Morrish, T. A. et al. DNA repair mediated by endonuclease-independent LINE-1 retrotransposition. Nat. Genet. 31, 159–165 (2002).
Google Scholar
Farkash, E. A. & Luning Prak, E. T. DNA damage and L1 retrotransposition. J. Biomed. Biotechnol. 2006, 37285 (2006).
Google Scholar
Suzuki, J. et al. Genetic evidence that the non-homologous end-joining repair pathway is involved in LINE retrotransposition. PLoS Genet. 5, e1000461 (2009).
Google Scholar
Baldwin, E. T. et al. Structures, functions and adaptations of the human LINE-1 ORF2 protein. Nature 626, 194–206 (2024).
Google Scholar
Freeman, B. et al. Analysis of epigenetic features characteristic of L1 loci expressed in human cells. Nucleic Acids Res. 50, 1888–1907 (2022).
Google Scholar
Rodgers, K. & McVey, M. Error-prone repair of DNA double-strand breaks. J. Cell. Physiol. 231, 15–24 (2016).
Google Scholar
Wangsri, S., Subbalekha, K., Kitkumthorn, N. & Mutirangura, A. Patterns and possible roles of LINE-1 methylation changes in smoke-exposed epithelia. PLoS ONE 7, e45292 (2012).
Google Scholar
Stueve, T. R. et al. Epigenome-wide analysis of DNA methylation in lung tissue shows concordance with blood studies and identifies tobacco smoke-inducible enhancers. Hum. Mol. Genet. 26, 3014–3027 (2017).
Google Scholar
Caliri, A. W., Caceres, A., Tommasi, S. & Besaratinia, A. Hypomethylation of LINE-1 repeat elements and global loss of DNA hydroxymethylation in vapers and smokers. Epigenetics 15, 816–829 (2020).
Google Scholar
Camila, B. et al. Genotoxicity and hypomethylation of LINE-1 induced by electronic cigarettes. Ecotoxicol. Environ. Saf. 256, 114900 (2023).
Google Scholar
Joehanes, R. et al. Epigenetic signatures of cigarette smoking. Circ. Cardiovasc. Genet. 9, 436–447 (2016).
Google Scholar
Imbeault, M., Helleboid, P.-Y. & Trono, D. KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks. Nature 543, 550–554 (2017).
Google Scholar
Han, G. et al. An atlas of epithelial cell states and plasticity in lung adenocarcinoma. Nature 627, 656–663 (2024).
Google Scholar
Long, E. et al. Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes. Nat. Commun. 15, 7995 (2024).
Google Scholar
Rosspopoff, O. & Trono, D. Take a walk on the KRAB side. Trends Genet. 39, 844–857 (2023).
Google Scholar
Hill, W. et al. Lung adenocarcinoma promotion by air pollutants. Nature 616, 159–167 (2023).
Google Scholar
Haga, Y. et al. Whole-genome sequencing reveals the molecular implications of the stepwise progression of lung adenocarcinoma. Nat. Commun. 14, 8375 (2023).
Google Scholar
Huang, Z. et al. Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking. Nat. Genet. 54, 492–498 (2022).
Google Scholar
Colom, B. et al. Mutant clones in normal epithelium outcompete and eliminate emerging tumours. Nature 598, 510–514 (2021).
Google Scholar
Jardim, D. L., Goodman, A., de Melo Gagliato, D. & Kurzrock, R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell 39, 154–173 (2021).
Google Scholar
Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626–638 (2016).
Google Scholar
Vaz, M. et al. Chronic cigarette smoke-induced epigenomic changes precede sensitization of bronchial epithelial cells to single-step transformation by KRAS mutations. Cancer Cell 32, 360–376 (2017).
Google Scholar
Mengs, U. Tumour induction in mice following exposure to aristolochic acid. Arch. Toxicol. 61, 504–505 (1988).
Google Scholar
Ambatipudi, S. et al. Tobacco smoking-associated genome-wide DNA methylation changes in the EPIC study. Epigenomics 8, 599–618 (2016).
Google Scholar
Kobayashi, S. et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 352, 786–792 (2005).
Google Scholar
Shah, N. M. et al. Pan-cancer analysis identifies tumor-specific antigens derived from transposable elements. Nat. Genet. 55, 631–639 (2023).
Google Scholar
Bergmann, E. A., Chen, B.-J., Arora, K., Vacic, V. & Zody, M. C. Conpair: concordance and contamination estimator for matched tumor-normal pairs. Bioinformatics 32, 3196–3198 (2016).
Google Scholar
Pedersen, B. S. et al. Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches. Genome Med. 12, 62 (2020).
Google Scholar
Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
Google Scholar
Dentro, S. C. et al. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell 184, 2239–2254 (2021).
Google Scholar
Sadedin, S. P. & Oshlack, A. Bazam: a rapid method for read extraction and realignment of high-throughput sequencing data. Genome Biol. 20, 78 (2019).
Google Scholar
Martínez-Jiménez, F. et al. A compendium of mutational cancer driver genes. Nat. Rev. Cancer 20, 555–572 (2020).
Google Scholar
Yuan, K., Macintyre, G., Liu, W., PCAWG-11 Working Group & Markowetz, F. Ccube: a fast and robust method for estimating cancer cell fractions. Preprint at bioRxiv https://doi.org/10.1101/484402 (2018).
Yang, L. et al. Diverse mechanisms of somatic structural variations in human cancer genomes. Cell 153, 919–929 (2013).
Google Scholar
Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).
Google Scholar
Muiños, F., Martínez-Jiménez, F., Pich, O., Gonzalez-Perez, A. & Lopez-Bigas, N. In silico saturation mutagenesis of cancer genes. Nature 596, 428–432 (2021).
Google Scholar
Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1, 1–16 (2017).
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 (2018).
Google Scholar
Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).
Google Scholar
Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genom. 20, 685 (2019).
Google Scholar
Islam, S. M. A. et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genom. 2, 100179 (2022).
Sondka, Z. et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Res. 52, D1210–D1217 (2024).
Google Scholar
Díaz-Gay, M. et al. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. Bioinformatics 39, btad756 (2023).
Degasperi, A. et al. Substitution mutational signatures in whole-genome-sequenced cancers in the UK population. Science 376, abl9283 (2022).
Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
Google Scholar
Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Google Scholar
Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022).
Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).
Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Google Scholar
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Google Scholar
Li, T. et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48, W509–W514 (2020).
Google Scholar
de Tribolet-Hardy, J. et al. Genetic features and genomic targets of human KRAB-zinc finger proteins. Genome Res. 33, 1409–1423 (2023).
Google Scholar
Del Toro, N. et al. The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Res. 50, D648–D653 (2022).
Google Scholar
Oleksiewicz, U. et al. TRIM28 and interacting KRAB-ZNFs control self-renewal of human pluripotent stem cells through epigenetic repression of pro-differentiation genes. Stem Cell Rep. 9, 2065–2080 (2017).
Google Scholar
McKerrow, W. & Fenyö, D. L1EM: a tool for accurate locus specific LINE-1 RNA quantification. Bioinformatics 36, 1167–1173 (2020).
Google Scholar
Reyes-Gopar, H. et al. A single-cell transposable element atlas of human cell identity. Cell Rep. Methods 5, 101086 (2025).
Müller, F. et al. RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome Biol. 20, 55 (2019).
Google Scholar
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
Google Scholar
Kawaguchi, S., Higasa, K., Shimizu, M., Yamada, R. & Matsuda, F. HLA-HD: an accurate HLA typing algorithm for next-generation sequencing data. Hum. Mutat. 38, 788–797 (2017).
Google Scholar
Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48, W449–W454 (2020).
Google Scholar
Schenck, R. O., Lakatos, E., Gatenbee, C., Graham, T. A. & Anderson, A. R. A. NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline. BMC Bioinform. 20, 264 (2019).
Google Scholar
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830 (2018).
Google Scholar
Buffa, F. M., Harris, A. L., West, C. M. & Miller, C. J. Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br. J. Cancer 102, 428–435 (2010).
Google Scholar
Winter, S. C. et al. Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers. Cancer Res. 67, 3441–3449 (2007).
Google Scholar
Ragnum, H. B. et al. The tumour hypoxia marker pimonidazole reflects a transcriptional programme associated with aggressive prostate cancer. Br. J. Cancer 112, 382–390 (2015).
Google Scholar
Elvidge, G. P. et al. Concordant regulation of gene expression by hypoxia and 2-oxoglutarate-dependent dioxygenase inhibition: the role of HIF-1α, HIF-2α, and other pathways. J. Biol. Chem. 281, 15215–15226 (2006).
Google Scholar
Sørensen, B. S., Toustrup, K., Horsman, M. R., Overgaard, J. & Alsner, J. Identifying pH independent hypoxia induced genes in human squamous cell carcinomas in vitro. Acta Oncol. 49, 895–905 (2010).
Google Scholar
■ مصدر الخبر الأصلي
نشر لأول مرة على: www.nature.com
تاريخ النشر: 2025-12-10 02:00:00
الكاتب: Tongwu Zhang
تنويه من موقع “yalebnan.org”:
تم جلب هذا المحتوى بشكل آلي من المصدر:
www.nature.com
بتاريخ: 2025-12-10 02:00:00.
الآراء والمعلومات الواردة في هذا المقال لا تعبر بالضرورة عن رأي موقع “yalebnan.org”، والمسؤولية الكاملة تقع على عاتق المصدر الأصلي.
ملاحظة: قد يتم استخدام الترجمة الآلية في بعض الأحيان لتوفير هذا المحتوى.
