Dataset Information

Radiotherapy-Related Gene Signature in Prostate Cancer.

ABSTRACT: Radiotherapy for localized prostate cancer has increased the cure and survival rates of patients. Besides its local tumoricidal effects, ionizing radiation has been linked to mechanisms leading to systemic immune activation, a phenomenon called the abscopal effect. In this study, we performed gene expression analysis on peripheral blood from prostate cancer patients obtained post- radiotherapy and showed that 6 genes, including CCR7, FCGR2B, BTLA, CD6, CD3D, and CD3E, were down-regulated by a range of 1.5-2.5-fold as compared to pre-radiotherapy samples. The expression of the signature consisting of these six genes was also significantly lower post- vs. pre-radiotherapy. These genes are involved in various tumor-promoting immune pathways and their down-regulation post-radiotherapy could be considered beneficial for patients. This is supported by the fact that low mRNA expression levels for the 6-gene signature in the prostate tumor tissue was linked to better survival. Importantly, we report that this 6-gene signature strongly correlated with a favorable prognosis regardless of poor standard clinicopathological parameters (i.e., Gleason score ≥ 8 and T3 (including T3a and T3b). Our pioneering data open the possibility that the 6-gene signature identified herein may have a predictive value, but this requires further long-term studies.

SUBMITTER: Fortis SP

PROVIDER: S-EPMC9599894 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Radiotherapy-Related Gene Signature in Prostate Cancer.

Fortis Sotirios P SP Goulielmaki Maria M Aubert Nicolas N Batsaki Panagiota P Ouzounis Sotirios S Cavouras Dionisis D Marodon Gilles G Stokidis Savvas S Gritzapis Angelos D AD Baxevanis Constantin N CN

Cancers 20221014 20

Radiotherapy for localized prostate cancer has increased the cure and survival rates of patients. Besides its local tumoricidal effects, ionizing radiation has been linked to mechanisms leading to systemic immune activation, a phenomenon called the abscopal effect. In this study, we performed gene expression analysis on peripheral blood from prostate cancer patients obtained post- radiotherapy and showed that 6 genes, including CCR7, FCGR2B, BTLA, CD6, CD3D, an ...[more]

PMID: 36291815

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Project description:BackgroundProstate cancer (PCa) is one of the most prevalent cancers that occur in men worldwide. Autophagy-related genes (ARGs) may play an essential role in multiple biological processes of prostate cancer. However, ARGs expression signature has rarely been used to investigate the association between autophagy and prognosis in PCa. This study aimed to identify and assess prognostic ARGs signature to predict overall survival (OS) and disease-free survival (DFS) in PCa patients.MethodsFirst, a total of 234 autophagy-related genes were obtained from The Human Autophagy Database. Then, differentially expressed ARGs were identified in prostate cancer patients based on The Cancer Genome Atlas (TCGA) database. The univariate and multivariate Cox regression analysis was performed to screen hub prognostic ARGs for overall survival and disease-free survival, and the prognostic model was constructed. Finally, the correlation between the prognostic model and clinicopathological parameters was further analyzed, including age, T status, N status, and Gleason score.ResultsThe OS-related prognostic model was constructed based on the five ARGs (FAM215A, FDD, MYC, RHEB, and ATG16L1) and significantly stratified prostate cancer patients into high- and low-risk groups in terms of OS (HR = 6.391, 95% CI = 1.581- 25.840, P < 0.001). The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.84. The OS-related prediction model values were higher in T3-4 than in T1-2 (P = 0.008), and higher in Gleason score > 7 than ≤ 7 (P = 0.015). In addition, the DFS-related prognostic model was constructed based on the 22 ARGs (ULK2, NLRC4, MAPK1, ATG4D, MAPK3, ATG2A, ATG9B, FOXO1, PTEN, HDAC6, PRKN, HSPB8, P4HB, MAP2K7, MTOR, RHEB, TSC1, BIRC5, RGS19, RAB24, PTK6, and NRG2), with AUC of 0.85 (HR = 7.407, 95% CI = 4.850-11.320, P < 0.001), which were firmly related to T status (P < 0.001), N status (P = 0.001), and Gleason score (P < 0.001).ConclusionsOur ARGs based prediction models are a reliable prognostic and predictive tool for overall survival and disease-free survival in prostate cancer patients.

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Project description:BackgroundThe tumor microenvironment (TME) has recently been proven to play a crucial role in the development and prognosis of tumors. However, the current knowledge on the potential of the TME in prostate cancer (PCa) remains scarce.PurposeThis study aims to elucidate the value of TME-related genes for PCa prognosis by integrative bioinformatics analysis.Materials and methodsWe downloaded the immune and stromal scores of PCa samples via the ESTIMATE and correlated these scores to clinicopathological characteristics and recurrence-free survival (RFS) of patients. Based on these scores, the TME-related differentially expressed genes were identified for functional enrichment analysis. Cox regression analyses were performed to identify prognostic genes and establish a predictive risk model. Moreover, gene set enrichment analysis (GSEA) was performed to evaluate the relationship between risk score and immune pathway.ResultsThe stromal and immune scores were associated with clinicopathological characteristics and RFS in PCa patients. In total, 238 intersecting differentially expressed genes were identified. Functional enrichment analysis further revealed that these genes dramatically participated in the immune-related pathways. The immune-related risk model was built with C-type lectin domain containing 7A (CLEC7A) and collagen type XI alpha 1 chain (COL11A1) using Cox regression analyses. Kaplan-Meier survival analysis showed that the expression levels of CLEC7A and COL11A1 were significantly associated with the RFS. Further, the RFS time in high-risk group was significantly shorter than that in low-risk group. The areas under the curve for the risk model in predicting 3- and 5-year RFS rates were 0.694 and 0.731, respectively. GSEA suggested that immunosuppression existed in high-risk PCa patients.ConclusionCLEC7A and COL11A1 were selected to build a predictive risk model, which may help clinicians to assess the prognosis of PCa patients and select appropriate targets for immunotherapy.

Dataset Information

Radiotherapy-Related Gene Signature in Prostate Cancer.

Publications

Radiotherapy-Related Gene Signature in Prostate Cancer.

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