[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-383733-105":59,"doc-detail-383733-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","globally-invariant-behavior-of-oncogenes-and-random-genes-at-population-but-not-at-single-cell-level","Globally invariant behavior of oncogenes and random genes at population but not at single cell level","","Cancer is widely viewed as a genetic disease, yet evidence suggests that many human genes can relate to cancer, making the separation of true oncogenic drivers from merely associated genes critical. This study compares single-cell and bulk transcriptome/proteome datasets across multiple cancers versus healthy controls. Bulk analyses with machine learning and statistics find invariant behavior for oncogenes and randomly selected gene sets, while protein–protein interaction analysis shows higher connectivity for oncogene-derived networks. At single-cell scale, oncogenes display variant behavior in subsets, emphasizing the need for protein causality and higher-resolution analyses.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/globally-invariant-behavior-of-oncogenes-and-random-genes-at-population-but-not-at-single-cell-level/383733/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/globally-invariant-behavior-of-oncogenes-and-random-genes-at-population-but-not-at-single-cell-level/383733.png","ImageObject",300,407,{"name":92,"@type":93},"\tJames","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-24",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What does the study compare when analyzing oncogenes and random genes?","Question",{"text":111,"@type":112},"It analyzes both single-cell and bulk (cell-population) transcriptome and proteome datasets from cancer samples and matched healthy counterparts, using oncogene sets defined by the Cancer Genes Census.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What is the main difference between bulk and single-cell results in the paper?",{"text":116,"@type":112},"In bulk datasets, oncogenes show invariant behavior similar to randomly selected gene sets of the same size, whereas at single-cell scale the oncogenes exhibit variant behavior in a subset of oncogenes for each cancer type.",{"name":118,"@type":109,"acceptedAnswer":119},"How do protein–protein interaction analyses distinguish oncogenes from random genes?",{"text":120,"@type":112},"Protein–protein interaction network analyses show oncogene-derived networks with higher connectivity than networks derived from random gene sets.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},383733,1790258943,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":41},2336474466412,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","[www.nature.com/npjsba](www.nature.com/npjsba)  \nARTICLE OPEN   \nGlobally invariant behavior of oncogenes and random genes at population but not at single cell level  \nOlga Sirbu1, Mohamed Helmy1,2, Alessandro Giuliani 3 and Kumar Selvarajoo 1,4,5 ✉  \n\n|  | Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein–protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at singlecell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type Moving forward, we concur that |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| .\u003Cbr>the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses. |  |  |\n|  | npj Systems Biology and Applications (2023)9:28; [https://doi.org/10.1038/s41540-023-00290-9](https://doi.org/10.1038/s41540-023-00290-9) |  |\n|  |  |  |\n\nINTRODUCTION  \nCancer is both a heterogeneous as well as a highly dynamic malady1–4. Even within the same cancer types, tumors can exhibit intratumoral variations5 (biological variations gained during the progression of the disease, i.e., variable histopathology), intertumoral variations6 (variation within the same cancer patient i.e., metastasis), and interpatient variations7 (variations among patients i.e., heterogeneity) . Furthermore, the lack of a clear understanding of cancer causality is another confounding factor that makes cancer research complex8. Due to this, and despite tremendous efforts, approaching the study and treatment of cancer is a delicate ordeal, with existing treatments not being foolproof and often working sporadically only on a subset of patients3.  \nTo address these challenges, researchers target genes that are associated with the hallmarks of cancer, such as increased cell proliferation and avoidance of cell death9. Proto-oncogenes represent an exemplar case as they are genes whose expression is fully physiological and only when mutated (with a consequent dysregulation of their original function) act as cancer-causing elements10. This is the case of tumor suppressor genes (TSG) whose original function of limiting cell proliferation and directing cells toward apoptosis, when abolished by mutation, promote cancer development11.  \nThe high interest in cancer research also adds complexity which is evident by the lack of a unified consensus definition of oncogenes. For example, the National Human Genome Research Institute’s definition is, “an oncogene is a mutated gene that has the potential to cause cancer”12, while the National Cancer Institute (NCI) defines them as, “a gene that is a mutated (changed) form of a gene involved in normal cell growth”11. NCI provides another broader definition, “an oncogene is a gene that has the potential to cause cancer without the requirement of a particular change in the gene sequence or in the gene expression”. Lastly, in Comprehensive Toxicology the term  \noncogene refers to “a gene that encodes a protein that is capable of transforming cells in cultures or inducing cancer in animals”13.  \nFor cataloging oncogenes, the Cancer Genes Census (CGC) lists genes that contain mutations “that have been causally implicated in cancer”14. ","cbCaivxBr9fOrNvR","https://ap.wps.com/l/cbCaivxBr9fOrNvR","pdf",2884277,12,"English","# Introduction\n## Oncogene definitions and database implications\n## Research motivation and study design\n# Methods and datasets\n## Bulk versus single-cell analyses\n## Machine learning and statistical testing\n## Protein–protein interaction network comparisons","[{\"question\":\"What does the study compare when analyzing oncogenes and random genes?\",\"answer\":\"It analyzes both single-cell and bulk (cell-population) transcriptome and proteome datasets from cancer samples and matched healthy counterparts, using oncogene sets defined by the Cancer Genes Census.\"},{\"question\":\"What is the main difference between bulk and single-cell results in the paper?\",\"answer\":\"In bulk datasets, oncogenes show invariant behavior similar to randomly selected gene sets of the same size, whereas at single-cell scale the oncogenes exhibit variant behavior in a subset of oncogenes for each cancer type.\"},{\"question\":\"How do protein–protein interaction analyses distinguish oncogenes from random genes?\",\"answer\":\"Protein–protein interaction network analyses show oncogene-derived networks with higher connectivity than networks derived from random gene sets.\"}]","Globally invariant behavior of oncogenes and random genes at population but not at single cell level | PDF"]