A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC
BackgroundMost patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune...
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1171582/full |
| _version_ | 1797780004834115584 |
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| author | Yue Xi Yingchun Zhang Kun Zheng Jiawei Zou Lv Gui Xin Zou Liang Chen Jie Hao Yiming Zhang |
| author_facet | Yue Xi Yingchun Zhang Kun Zheng Jiawei Zou Lv Gui Xin Zou Liang Chen Jie Hao Yiming Zhang |
| author_sort | Yue Xi |
| collection | DOAJ |
| description | BackgroundMost patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.MethodsTwo HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).ResultsBy combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.ConclusionsThe developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients. |
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| language | English |
| last_indexed | 2024-03-12T23:38:31Z |
| publishDate | 2023-07-01 |
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| series | Frontiers in Oncology |
| spelling | doaj.art-311dcfd0492c4538acd9e73b505d73542023-07-15T03:31:43ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-07-011310.3389/fonc.2023.11715821171582A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOCYue Xi0Yingchun Zhang1Kun Zheng2Jiawei Zou3Lv Gui4Xin Zou5Liang Chen6Jie Hao7Yiming Zhang8Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Urology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaInstitute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Pathology, Jinshan Hospital, Fudan University, Shanghai, ChinaJinshan Hospital Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, ChinaDepartment of Gynecological Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, ChinaInstitute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaBackgroundMost patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.MethodsTwo HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).ResultsBy combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.ConclusionsThe developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.https://www.frontiersin.org/articles/10.3389/fonc.2023.1171582/fullchemotherapysingle-cell RNA-seqhigh-grade serous ovarian cancer (HGSOC)bioinformaticsresponse prediction model |
| spellingShingle | Yue Xi Yingchun Zhang Kun Zheng Jiawei Zou Lv Gui Xin Zou Liang Chen Jie Hao Yiming Zhang A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC Frontiers in Oncology chemotherapy single-cell RNA-seq high-grade serous ovarian cancer (HGSOC) bioinformatics response prediction model |
| title | A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC |
| title_full | A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC |
| title_fullStr | A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC |
| title_full_unstemmed | A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC |
| title_short | A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC |
| title_sort | chemotherapy response prediction model derived from tumor promoting b and tregs and proinflammatory macrophages in hgsoc |
| topic | chemotherapy single-cell RNA-seq high-grade serous ovarian cancer (HGSOC) bioinformatics response prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1171582/full |
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