Frontiers in oncology · 2025
Integration of autophagy-related genes and immune dysregulation reveals a prognostic landscape in multiple myeloma.
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Xia Yibo, Zheng Dong, Zhang Xinyi, Zhu Shuxia, Lan Enqing, Ying Hansen, Chen Zixing, Zhang Bingxin, Zhou Shujuan, Zhang Yu, Lin Xuanru, Zhuang Qiang, Qian Honglan, Hu Xudong, Zhuang Yan, Zhang Qianying, Zhou Xiangjing, Xie Zuoting, Jiang Songfu, Ma Yongyong, Jin Zhouxiang, Zheng Sisi
Аннотация
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.
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.