{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T15:11:26Z","timestamp":1788793886666,"version":"build-2803163510"},"reference-count":44,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Incorporating ultrasound (US) treatment sensitivity analysis may improve the treatment of acute myeloid leukemia (AML).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study integrated single-cell and bulk datasets for analysis. Differential expression analysis between US-treated and control samples was performed using limma package. The AUCell package was used to calculate US-associated scores in the single-cell dataset. Differentially expressed genes (DEGs) between the specific groups were identified, followed by intersection analysis with previously identified DEGs. Univariate regression, Least Absolute Shrinkage and Selection Operator (LASSO) analysis (using the glmnet package), and stepwise multivariate regression (using the MASS package) were used to refine the candidate genes and to construct a risk model. The model genes were validated using\n                      <jats:italic>in vitro<\/jats:italic>\n                      experiments. Enrichment analysis was conducted using gene set enrichment analysis (GSEA), and immune infiltration was evaluate by single-sample GSEA (ssGSEA) and ESTIMATE algorithms. The correlations between RiskScores and drug sensitivity were analyzed by oncoPredict package. Finally, tumor mutational burden (TMB) and genomic mutations were compared between the risk groups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Nine prognostic signatures (\n                      <jats:italic>SPINK2<\/jats:italic>\n                      ,\n                      <jats:italic>HNRNPAB<\/jats:italic>\n                      ,\n                      <jats:italic>SH3BGRL3<\/jats:italic>\n                      ,\n                      <jats:italic>CLEC11A<\/jats:italic>\n                      ,\n                      <jats:italic>ITGA4<\/jats:italic>\n                      ,\n                      <jats:italic>RPL39L<\/jats:italic>\n                      ,\n                      <jats:italic>MX1<\/jats:italic>\n                      ,\n                      <jats:italic>HEXIM1<\/jats:italic>\n                      , and\n                      <jats:italic>MAP4K4<\/jats:italic>\n                      ) were identified. Particularly, low expression of\n                      <jats:italic>SPINK2<\/jats:italic>\n                      attenuated the activity and invasion of AML cells. High-risk group had higher immune cell infiltration. Eight drugs were predicted to be correlated with the RiskScore model.\n                      <jats:italic>DNMT3A<\/jats:italic>\n                      and\n                      <jats:italic>RUNX1<\/jats:italic>\n                      showed higher mutation frequencies in the high-risk group, whereas\n                      <jats:italic>KIT<\/jats:italic>\n                      and\n                      <jats:italic>MUC16<\/jats:italic>\n                      showed higher mutation frequencies in the low-risk group.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The RiskScore model established in this study provides a theoretical basis for clinically screening responsive populations and optimizing treatment strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj.21661","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T14:59:15Z","timestamp":1788793155000},"page":"e21661","source":"Crossref","is-referenced-by-count":0,"title":["Identification of ultrasound-associated gene candidates in myeloid cells and construction of a prognostic risk model for acute myeloid leukemia"],"prefix":"10.7717","volume":"14","author":[{"given":"Lijia","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Ultrasound, Jiangnan Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China"},{"name":"Department of Ultrasound, Hangzhou Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghua","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Jiangnan Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China"},{"name":"Department of Ultrasound, Hangzhou Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"issue":"11","key":"10.7717\/peerj.21661\/ref-1","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nature Methods"},{"issue":"9","key":"10.7717\/peerj.21661\/ref-2","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.31557\/APJCP.2023.24.9.3077","article-title":"Potential candidate genes for therapeutic targeting in chronic myeloid leukemia: a pilot study","volume":"24","author":"Alsamman","year":"2023","journal-title":"Asian Pacific Journal of Cancer Prevention"},{"key":"10.7717\/peerj.21661\/ref-3","doi-asserted-by":"publisher","first-page":"101503","DOI":"10.1016\/j.lanepe.2025.101503","article-title":"Epidemiology, survival, and treatment of acute myeloid and lymphoblastic leukaemia in Germany: a nationwide population-based registry analysis","volume":"59","author":"Baden","year":"2025","journal-title":"The Lancet Regional Health. 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