{"title":"Integration of autophagy-related genes and immune dysregulation reveals a prognostic landscape in multiple myeloma.","authors":"Yibo Xia, Dong Zheng, Xinyi Zhang, Shuxia Zhu, Enqing Lan, Hansen Ying, Zixing Chen, Bingxin Zhang, Shujuan Zhou, Yu Zhang, Xuanru Lin, Qiang Zhuang, Honglan Qian, Xudong Hu, Yan Zhuang, Qianying Zhang, Xiangjing Zhou, Zuoting Xie, Songfu Jiang, Yongyong Ma, Zhouxiang Jin, Sisi Zheng","doi":"10.3389/fonc.2025.1635596","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Autophagy is a self-renewal mechanism in which cells degrade damaged organelles or abnormal proteins through lysosomes. This process eliminates harmful components within the cell and maintains energy homeostasis. Multiple myeloma (MM) is a hematological malignancy characterized by uncontrolled plasma cell proliferation. Autophagy plays a dual role in tumorigenesis, yet its prognostic implications in MM remain underexplored.</p><p><strong>Methods: </strong>Transcriptomic and clinical data from 1,386 MM patients (training cohort: GSE136337, n = 415; validation cohorts: GSE24080, n = 558; GSE4581, n = 413) were analyzed. A seven-gene signature (ATIC, CDKN1A, DNAJB9, EDEM1, GABARAPL1, RAB1A, VAMP7) was identified using LASSO-Cox regression. Predictive performance of the autophagy-related model was assessed via Kaplan-Meier analysis, ROC curves, and nomograms. Immune infiltration, drug sensitivity, and functional pathways of the autophagy-related model were evaluated using CIBERSORT, ESTIMATE, and GSEA. The gene expression in the autophagy prognostic model was verified by qRT-PCR in the U266 and RPMI8226 cell lines and blood samples of multiple myeloma patients from the First Affiliated Hospital of Wenzhou Medical University.</p><p><strong>Results: </strong>The autophagy-related risk score stratified patients into high-risk and low-risk groups with distinct survival outcomes (high-risk HR = 0.391, 95%CI:0.284-0.540, p < 0.001). The model demonstrated robust predictive accuracy (5-year AUC = 0.729) and was independently validated. High-risk patients exhibited elevated immune checkpoint expression (CD48, CD70, BTLA), stromal infiltration, and drug resistance. Functional enrichment linked high-risk profiles to MYC activation and oxidative phosphorylation. Through qRT-PCR, the accuracy of the autophagy-related model has been verified in the U266 and RPMI8226 cell lines, as well as in the blood samples of multiple myeloma patients from the First Affiliated Hospital of Wenzhou Medical University.</p><p><strong>Conclusion: </strong>This autophagy-related gene signature provides a reliable prognostic tool for MM, highlighting immune dysregulation and therapeutic resistance mechanisms. Its integration with clinical parameters enhances risk stratification and treatment planning.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1635596"},"PeriodicalIF":3.5000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483933/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fonc.2025.1635596","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Autophagy is a self-renewal mechanism in which cells degrade damaged organelles or abnormal proteins through lysosomes. This process eliminates harmful components within the cell and maintains energy homeostasis. Multiple myeloma (MM) is a hematological malignancy characterized by uncontrolled plasma cell proliferation. Autophagy plays a dual role in tumorigenesis, yet its prognostic implications in MM remain underexplored.
Methods: Transcriptomic and clinical data from 1,386 MM patients (training cohort: GSE136337, n = 415; validation cohorts: GSE24080, n = 558; GSE4581, n = 413) were analyzed. A seven-gene signature (ATIC, CDKN1A, DNAJB9, EDEM1, GABARAPL1, RAB1A, VAMP7) was identified using LASSO-Cox regression. Predictive performance of the autophagy-related model was assessed via Kaplan-Meier analysis, ROC curves, and nomograms. Immune infiltration, drug sensitivity, and functional pathways of the autophagy-related model were evaluated using CIBERSORT, ESTIMATE, and GSEA. The gene expression in the autophagy prognostic model was verified by qRT-PCR in the U266 and RPMI8226 cell lines and blood samples of multiple myeloma patients from the First Affiliated Hospital of Wenzhou Medical University.
Results: The autophagy-related risk score stratified patients into high-risk and low-risk groups with distinct survival outcomes (high-risk HR = 0.391, 95%CI:0.284-0.540, p < 0.001). The model demonstrated robust predictive accuracy (5-year AUC = 0.729) and was independently validated. High-risk patients exhibited elevated immune checkpoint expression (CD48, CD70, BTLA), stromal infiltration, and drug resistance. Functional enrichment linked high-risk profiles to MYC activation and oxidative phosphorylation. Through qRT-PCR, the accuracy of the autophagy-related model has been verified in the U266 and RPMI8226 cell lines, as well as in the blood samples of multiple myeloma patients from the First Affiliated Hospital of Wenzhou Medical University.
Conclusion: This autophagy-related gene signature provides a reliable prognostic tool for MM, highlighting immune dysregulation and therapeutic resistance mechanisms. Its integration with clinical parameters enhances risk stratification and treatment planning.
期刊介绍:
Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.