{"doi":"10.1172/jci139232","title":"Integrating imaging and RNA-seq improves outcome prediction in cervical cancer","abstract":"Approaches using a single type of data have been applied to classify human tumors. Here we integrate imaging features and transcriptomic data using a prospectively collected tumor bank. We demonstrate that increased maximum standardized uptake value on pretreatment 18F-fluorodeoxyglucose-positron emission tomography correlates with epithelial-to-mesenchymal transition (EMT) gene expression. We derived and validated 3 major molecular groups, namely squamous epithelial, squamous mesenchymal, and adenocarcinoma, using prospectively collected institutional (n = 67) and publicly available (n = 304) data sets. Patients with tumors of the squamous mesenchymal subtype showed inferior survival outcomes compared with the other 2 molecular groups. High mesenchymal gene expression in cervical cancer cells positively correlated with the capacity to form spheroids and with resistance to radiation. CaSki organoids were radiation-resistant but sensitive to the glycolysis inhibitor, 2-DG. These experiments provide a strategy for response prediction by integrating large data sets, and highlight the potential for metabolic therapy to influence EMT phenotypes in cervical cancer.","journal":"Journal of Clinical Investigation","year":2021,"id":176162,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8938,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":357049,"name":"Ramachandran Rashmi","orcid":"0000-0002-1753-6034","position":1,"is_corresponding":false},{"id":447965,"name":"Matthew Inkman","orcid":"0000-0001-5490-2374","position":2,"is_corresponding":false},{"id":357050,"name":"Kay Jayachandran","orcid":"0000-0003-3160-4299","position":3,"is_corresponding":false},{"id":357054,"name":"Fiona Ruiz","orcid":"0000-0003-3807-4980","position":4,"is_corresponding":false},{"id":718162,"name":"Michael R. Waters","orcid":"0000-0001-5070-2244","position":5,"is_corresponding":false},{"id":707413,"name":"Perry W. Grigsby","orcid":"0000-0001-9734-5088","position":6,"is_corresponding":false},{"id":707412,"name":"Stephanie Markovina","orcid":"0000-0002-1139-5533","position":7,"is_corresponding":false},{"id":227904,"name":"Julie K. Schwarz","orcid":"0000-0003-2407-7531","position":8,"is_corresponding":false},{"id":31913,"name":"Jin Zhang","orcid":"0000-0002-9117-2793","position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-18T23:47:19.591542Z","pmid":"33645544","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}