{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T16:45:26Z","timestamp":1777481126932,"version":"3.51.4"},"reference-count":25,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancer Biotherapy and Radiopharmaceuticals"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>The molecular mechanisms driving hepatocellular carcinoma (HCC) predicting chemotherapy sensitivity remain unclear; therefore, identification of these key biomarkers is essential for early diagnosis and treatment of HCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method:<\/jats:title>\n                    <jats:p>We collected and processed Computed Tomography (CT) and clinical data from 116 patients with autoimmune hepatitis (AIH) and HCC who came to our hospital\u2019s Liver Cancer Center. We then identified and extracted important characteristic features of significant patient images and correlated them with mitochondria-related genes using machine learning techniques such as multihead attention networks, lasso regression, principal component analysis (PCA), and support vector machines (SVM). These genes were integrated into radiomics signature models to explore their role in disease progression. We further correlated these results with clinical variables to screen for driver genes and evaluate the predict ability of chemotherapy sensitive of key genes in liver cancer (LC) patients. Finally, qPCR was used to validate the expression of this gene in patient samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Our study utilized attention networks to identify disease regions in medical images with 97% accuracy and an AUC of 94%. We extracted 942 imaging features, identifying five key features through lasso regression that accurately differentiate AIH from HCC. Transcriptome analysis revealed 132 upregulated and 101 downregulated genes in AIH, with 45 significant genes identified by XGBOOST. In HCC analysis, PCA and random forest highlighted 11 key features. Among mitochondrial genes,\n                      <jats:italic toggle=\"yes\">SLC25A42<\/jats:italic>\n                      correlated positively with normal tissue imaging features but negatively with cancerous tissues and was identified as a driver gene. Low expression of\n                      <jats:italic toggle=\"yes\">SLC25A42<\/jats:italic>\n                      was associated with chemotherapy sensitive in HCC patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>\n                      In conclusion, machine learning modeling combined with genomic profiling provides a promising approach to identify the driver gene\n                      <jats:italic toggle=\"yes\">SLC25A42<\/jats:italic>\n                      in LC, which may help improve diagnostic accuracy and chemotherapy sensitivity for this disease.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/cbr.2025.0094","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T04:45:42Z","timestamp":1748839542000},"page":"605-612","source":"Crossref","is-referenced-by-count":0,"title":["Exploring\n                    <i>SLC25A42<\/i>\n                    as a Radiogenomic Marker from the Perioperative Stage to Chemotherapy in Hepatitis-Related Hepatocellular Carcinoma"],"prefix":"10.1177","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3521-4928","authenticated-orcid":false,"given":"Lei","family":"Dou","sequence":"first","affiliation":[{"name":"Department of Rheumatology and Immunology, The Second People\u2019s Hospital of Wuhu, Wuhu, P.R. China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, P.R. China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbing","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, P.R. China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology, The Second Affiliated Hospital of Guilin Medical University, Guilin, P.R. China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, P.R. China."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2020.02.068"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.2147\/JHC.S61146"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2468-1253(22)00050-4"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.18"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.1998"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s15010-019-01345-y"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2017.02.006"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.4254\/wjh.v8.i28.1157"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1159\/000444471"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.4254\/wjh.v7.i16.1974"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/cr.2017.155"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2019.11.029"},{"issue":"5","key":"e_1_3_5_14_2","first-page":"14614","article-title":"Unveiling Early Detection And Prevention Of Cancer","volume":"30","author":"Khandakar S","year":"2024","unstructured":"Khandakar S, , Al Mamun MA, , Islam MM, et al. Unveiling Early Detection And Prevention Of Cancer. Machine Learning And Deep Learning Approaches. Educational Administration: Theory and Practice, 2024; 30(5):14614\u201314628.","journal-title":"Machine Learning And Deep Learning Approaches. 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Binding of lactoferrin to IGBP1 triggers apoptosis in a lung adenocarcinoma cell line. Anticancer Research, 2011; 31(2):529\u2013534.","journal-title":"Anticancer Research"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-016-2851-7"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2022.05.040"},{"key":"e_1_3_5_26_2","doi-asserted-by":"publisher","DOI":"10.1097\/HEP.0000000000000314"}],"container-title":["Cancer Biotherapy and Radiopharmaceuticals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1089\/cbr.2025.0094","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/cbr.2025.0094","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/cbr.2025.0094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T14:11:42Z","timestamp":1777385502000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.1089\/cbr.2025.0094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":25,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1089\/cbr.2025.0094"],"URL":"https:\/\/doi.org\/10.1089\/cbr.2025.0094","relation":{},"ISSN":["1084-9785","1557-8852"],"issn-type":[{"value":"1084-9785","type":"print"},{"value":"1557-8852","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}