Integrating imaging and RNA-seq improves outcome prediction in cervical cancer.
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ABSTRACT:
SUBMITTER: Zhang J
PROVIDER: S-EPMC7919714 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGamma delta (γδ) T cells play dual roles in human tumors, with both antitumor and tumor-promoting functions. However, the role of γδT cells in HPV-infected cervical cancer is still undetermined. Therefore, we aimed to identify γδT cell-related prognostic signatures in the cervical tumor microenvironment.MethodsSingle-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data, and corresponding clinical information of cervical cancer patients were obtained from the TCGA and GEO databases. The Seurat R package was used for single-cell analysis, and machine learning algorithms were used to screen and construct a γδT cell-related prognostic signature. Real-time quantitative PCR (RT-qPCR) was performed to detect the expression of prognostic signature genes.ResultsSingle-cell analysis indicated distinct populations of γδT cells between HPV-positive (HPV+) and HPV-negative (HPV-) cervical cancers. A trajectory analysis indicated γδT cells clustered into differential clusters with the pseudotime. High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) identified the key γδT cell-related gene modules. Bulk RNA-seq analysis also demonstrated the heterogeneity of immune cells, and the γδT-score was positively associated with inflammatory response and negatively associated with MYC stemness. Eight γδT cell-related hub genes (GTRGs), including ITGAE, IKZF3, LSP1, NEDD9, CLEC2D, RBPJ, TRBC2, and OXNAD1, were selected and validated as a prognostic signature for cervical cancer.ConclusionWe identified γδT cell-related prognostic signatures that can be considered independent factors for survival prediction in cervical cancer.
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Project description:BackgroundEmerging biomarkers from medical imaging or molecular characterization of tumour biopsies open up for combining the two and exploiting their synergy in treatment planning of cancer patients. We generated a paired data set of imaging- and gene-based hypoxia biomarkers in cervical cancer, appraised the influence of intratumour heterogeneity in patient classification, and investigated the benefit of combining the methodologies in prediction of chemoradiotherapy failure.MethodsHypoxic fraction from dynamic contrast enhanced (DCE)-MR images and an expression signature of six hypoxia-responsive genes were assessed as imaging- and gene-based biomarker, respectively in 118 patients.FindingsDichotomous biomarker cutoff to yield similar hypoxia status by imaging and genes was defined in 41 patients, and the association was validated in the remaining 77 patients. The two biomarkers classified 75% of 118 patients with the same hypoxia status, and inconsistent classification was not related to imaging-defined intratumour heterogeneity in hypoxia. Gene-based hypoxia was independent on tumour cell fraction in the biopsies and showed minor heterogeneity across multiple samples in 9 tumours. Combining imaging- and gene-based classification gave a significantly better prediction of PFS than one biomarker alone. A combined dichotomous biomarker optimized in 77 patients showed a large separation in PFS between more and less hypoxic tumours, and separated the remaining 41 patients with different PFS. The combined biomarker showed prognostic value together with tumour stage in multivariate analysis.InterpretationCombining imaging- and gene-based biomarkers may enable more precise and informative assessment of hypoxia-related chemoradiotherapy resistance in cervical cancer.FundingNorwegian Cancer Society, South-Eastern Norway Regional Health Authority, and Norwegian Research Council.
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