scholarly journals A Radiosensitivity Gene Signature and PD-L1 Status Predict Clinical Outcome of Patients with Glioblastoma Multiforme in The Cancer Genome Atlas Dataset

2020 ◽  
Vol 52 (2) ◽  
pp. 530-542 ◽  
Author(s):  
Bum-Sup Jang ◽  
In Ah Kim

PurposeCombination of radiotherapy and immune checkpoint blockade such as programmed death- 1 (PD-1) or programmed death-ligand 1 (PD-L1) blockade is being actively tested in clinical trial. We aimed to identify a subset of patients that could potentially benefit from this strategy using The Cancer Genome Atlas (TCGA) dataset for glioblastoma (GBM).Materials and MethodsA total of 399 cases were clustered into radiosensitive versus radioresistant (RR) groups based on a radiosensitivity gene signature and were also stratified as PD-L1 high versus PD-L1 low groups by expression of CD274 mRNA. Differential and integrated analyses with expression and methylation data were performed. CIBERSORT was used to enumerate the immune repertoire that resulted from transcriptome profiles.ResultsWe identified a subset of GBM, PD-L1-high-RR group which showed worse survival compared to others. In PD-L1-high-RR, differentially expressed genes (DEG) were highly enriched for immune response and mapped into activation of phosphoinositide 3-kinase–AKT and mitogen-activated protein kinase (MAPK) signaling pathways. Integration of DEG and differentially methylated region identified that the kinase MAP3K8-involved in T-cell receptor signaling was upregulated and BAI1, a factor which inhibits angiogenesis, was silenced. CIBERSORT showed that a higher infiltration of the immune repertoire, which included M2 macrophages and regulatory T cells.ConclusionTaken together, PD-L1-high-RR group could potentially benefit from radiotherapy combined with PD-1/PD-L1 blockade and angiogenesis inhibition.

2020 ◽  
Vol 14 (1) ◽  
pp. 53-63
Author(s):  
Shuai Li ◽  
Yue Teng ◽  
Min-Jie Yuan ◽  
Ting-Ting Ma ◽  
Jian Ma ◽  
...  

Aim: This study profiled differentially expressed long noncoding RNAs (lncRNAs) in lung squamous cell carcinoma (LSCC) to predict LSCC overall survival (OS) using The Cancer Genome Atlas data. Materials & methods: The RNA-seq and clinical dataset of 475 LSCC patients was retrieved from The Cancer Genome Atlas database and statistically analyzed. Results: There were 67 upregulated and 32 downregulated lncRNAs in LSCCs and 12 lncRNAs associated with OS. The seven-lncRNA signature was associated with poor OS and RP11-150O12.6 and CTA-384D8.35 were associated with better OS (p < 0.001). The seven lncRNAs-mRNA interaction network analysis showed their association with 187 protein-coding genes for cancer development, cell migration, adhesion, proliferation, apoptosis, angiogenesis and the MAPK signaling pathways. Conclusion: This seven-lncRNA signature is useful to predict LSCC OS.


Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 107
Author(s):  
Jungho Kim

Breast cancer is the most common cancer among women worldwide. MicroRNAs (miRNAs or miRs) play an important role in tumorigenesis, and thus, they have been identified as potential targets for translational research with diagnostic, prognostic, and therapeutic markers. This study aimed to identify differentially expressed (DE) miRNAs in breast cancer using the Cancer Genome Atlas. The miRNA profiles of 755 breast cancer tissues and 86 adjacent non-cancerous breast tissues were analyzed using Multi Experiment Viewer; miRNA–mRNA network analyses and constructed KEGG pathways with the predicted target genes were performed. The clinical relevance of miRNAs was investigated using area under the receiver operating characteristic curve (AUC) analysis, sensitivity, and specificity. The analysis identified 28 DE miRNAs in breast cancer tissues, including nine upregulated and 19 downregulated miRNAs, compared to non-cancerous breast tissues (p < 0.001). The AUC for each DE miRNA, miR-10b, miR-21, miR-96, miR-99a, miR-100, miR-125b-1, miR-125b-2, miR-139, miR-141, miR-145, miR-182, miR-183, miR-195, miR-200a, miR-337, miR-429, and let-7c, exceeded 0.9, indicating excellent diagnostic performance in breast cancer. Moreover, 1381 potential target genes were predicted using the prediction database tool, miRNet. These genes are related to PD-L1 expression and PD-1 checkpoint in cancer, MAPK signaling, apoptosis, and TNF pathways; hence, they regulate the development, progression, and immune escape of cancer. Thus, these 28 miRNAs can serve as prospective biomarkers for the diagnosis of breast cancer. Taken together, these results provide insight into the pathogenic mechanisms and potential therapies for breast cancer.


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