ArticleTranslational oncology2023
Comprehensive analysis for the immune related biomarkers of platinum-based chemotherapy in ovarian cancer.
Article in Translational oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.Nature communications · 2024Article
- Identification of PANoptosis-related biomarkers and analysis of prognostic values in head and neck squamous cell carcinoma.Scientific reports · 2024Article
- Role of long non-coding RNA in chemoradiotherapy resistance of nasopharyngeal carcinoma.Frontiers in oncology · 2024Review
- Integrated analysis of histone lysine lactylation (Kla)-specific genes suggests that NR6A1, OSBP2 and UNC119B are novel therapeutic targets for hepatocellular carcinoma.Scientific reports · 2023Article
- Ovarian cancer treatment and natural killer cell-based immunotherapy.Frontiers in immunology · 2023Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer (OC) is one of the most lethal gynecological malignancies. This study aimed to identify biomarkers that were sensitive to platinum-based chemotherapeutic agents and can be used in immunotherapy and explore the importance of their mechanisms of action.
methodsRNA-seq profiles and clinicopathological data for OC samples were obtained from The Cancer Genome Atlas (TCGA) and cBioPortal platform, respectively. Platinum-sensitive and platinum-resistant OC samples in the TCGA cohort were selected based on the clinical information. RNA-seq data for 70 OC samples withSingle-sample gene set enrichment analysis (ssGSEA) and unsupervised clustering were used to classify OC patients from the TCGA cohort into clusters with different proportions of infiltrating immune cells. ESTIMATE analysis was used to assess the immune landscape among clusters. Differential expression, univariate Cox regression, and LASSO regression analyses were performed to construct prognostic model. Spearman correlation analysis was conducted to investigate the correlations among immune checkpoint inhibitors (ICIs) and risk score, half-maximal drug inhibitory concentration (IC
resultsUsing ssGSEA and unsupervised clustering, OC samples were divided into two clusters with different immune cell infiltration. Then, 1715 differentially expressed immune-related genes (DEIRGs) were identified between two clusters, 984 differentially expressed platinum-sensitive related genes (DEPSRGs) between 149 platinum-sensitive and 63 platinum-resistant OC samples were identified, and 5384 differentially expressed genes (DEGs) between 380 OC and 194 normal samples were detected from the TCGA cohort. Six biomarkers (GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB) were detected to establish a prognostic model. The OC patients in the TCGA cohort were classified into high- and low-risk groups. The receive operating characteristic (ROC) curve was plotted and demonstrated that the prognostic model performed well with the area under ROC curve (AUC) greater than 0.6. The expressions of 5 ICIs, including CD200, TNFRSF18, CD160, CD200R1, and CD274 (PD-L1), were significantly different between two risk groups, and the risk score was significant negative associated with CTLA4, TNFRSF4, TNFRSF18, and CD274. Moreover, there were significant differences in IC
conclusionIn summary, this study constructed a novel prognostic model based on six prognostic biomarkers, including GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB, which can be utilized for predicting the prognosis of OC patients. These biomarkers were the potential therapeutic targets.
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