Identification of novel genes in testicular cancer microenvironment based on ESTIMATE algorithm‐derived immune scores

Ke, Zhi‐Bin; Wu, Yupeng; Huang, Peng; Hou, Jian; Chen, Ye‐Hui; Dong, Ru‐Nan; Lin, Fei; Wei, Yong; Xue, Xue‐Yi; Ng, Chi‐Fai; Xu, Ning

doi:10.1002/jcp.29898

Cited by 39 publications

(31 citation statements)

References 38 publications

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“…Based on functional analysis, KEGG pathway enrichment analysis showed the DEGs were mainly enriched in cell adhesion molecules [ 39 ], JAK-STAT signaling pathway [ 40 ], MAPK signaling pathway [ 41 ], PI3K-Akt signaling pathway [ 42 ], ECM-receptor interaction [ 43 ], complement and coagulation cascades [ 44 , 45 ], focal adhesion [ 46 – 48 ], and so on, which were not only iron-related but also immune-related. Interestingly, DEGs between high risk group and low risk group were found enriched in several immune-related GO terms such as adaptive immune response [ 49 ], immune response-activating cell surface receptor signaling pathway [ 50 ], immune response-activating signal transduction, lymphocyte mediated immunity, regulation of cell growth, regulation of immune effector process, and so on.…”

Section: Discussionmentioning

confidence: 99%

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma

Yang

Tian

Chen

et al. 2021

Journal of Oncology

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Background. Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecologic cancer death in women around the world. Ferroptosis, an iron-dependent regulated cell death, plays a vital role in the development of many cancers. Applying expression of ferroptosis-related gene to forecast the cancer progression is helpful for cancer treatment. However, the relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC. Methods. The Cancer Genome Atlas (TCGA) data of OC were obtained and the datasets were randomly divided into training and test datasets. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. The test dataset and ICGC dataset were used to validate this signature. Results. We constructed a model containing nine ferroptosis-related genes, namely, LPCAT3, ACSL3, CRYAB, PTGS2, ALOX12, HSBP1, SLC1A5, SLC7A11, and ZEB1, and predicted the OS of OC in TCGA. At a suitable cutoff, patients were divided into low risk and high risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics (ROCs) were as high as 0.664, respectively. Then, the test dataset and the ICGC dataset were used to evaluate our model, and the ROCs of test dataset were 0.667 and 0.777, respectively. In addition, functional analysis and correlation analysis showed that immune-related pathways were significantly enriched. Meanwhile, we also integrated with other clinical factors and we found the synthesized clinical factors and ferroptosis-related gene signature improved prognostic accuracy relative to the ferroptosis-related gene signature alone. Conclusion. The ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making.

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Section: Discussionmentioning

confidence: 99%

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma

Yang

Tian

Chen

et al. 2021

Journal of Oncology

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“…The ESTIMATE algorithm has been used for various cancers, suggesting that it is effective and robust based on the expression profiles. Utilizing this algorithm, Ke et al identified novel immune-related genes such as LINC01564, LINC02208 and ODAM for testicular cancer (16). Wang et al developed a stromal and immune score-related gene signature composed of SOX9, LRRC32, CECR1, and MS4A4A for gastric cancer (17).…”

Section: Discussionmentioning

confidence: 99%

C3AR1 mRNA as a Potential Therapeutic Target Associates With Clinical Outcomes and Tumor Microenvironment in Osteosarcoma

Zou

Liu²,

Wang³

et al. 2021

Front. Med.

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Objective: Targeting cancer-specific messenger RNAs (mRNAs) may offer novel insights into therapeutic strategies in osteosarcoma. This study aimed to discover possible osteosarcoma-specific mRNA and probe its biological functions.Methods: Based on mRNA-seq data from the TARGET database, stromal and immune scores were estimated for each osteosarcoma sample via the ESTIMATE algorithm. Stromal and immune mRNAs were obtained via integration of differentially expressed mRNAs between high and low stromal / immune score groups. Among hub and prognostic mRNAs, C3AR1 mRNA was focused and its prognostic value was assessed. The associations between C3AR1 mRNA and immune cells were analyzed via the CIBERSORT algorithm. Its expression was verified in osteosarcoma tissues and cells by RT-qPCR and western blot. The functions of C3AR1 were investigated by a series of experiments.Results: Low stromal and immune scores were both indicative of unfavorable outcomes for osteosarcoma patients. Eighty-eight up-regulated and seven down-regulated stromal and immune mRNAs were identified. Among 30 hub mRNAs, low expression of C3AR1 mRNA indicated worse outcomes than its high expression. There was a lower mRNA expression of C3AR1 in metastatic than non-metastatic osteosarcoma. C3AR1 mRNA was closely correlated to various immune cells such as macrophages. C3AR1 was verified to be down-regulated in osteosarcoma tissues and cells. Its overexpression suppressed proliferation, migration and invasion and induced apoptosis in osteosarcoma cells.Conclusion: C3AR1 mRNA could be a promising therapeutic target for osteosarcoma, linked with prognosis and tumor microenvironment.

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“…In this study, we divided all the cases downloaded from the TCGA platform into two different groups marked high and low immune/stromal scores, respectively. Then, 908 DEGs (900 upregulation and eight down-regulation) were screened from the immune score and stromal score based on the ESTIMATE algorithm [8]. [18,19].…”

Section: Discussionmentioning

confidence: 99%

“…Then the ESTIMATE algorithm (http://r-forge.r-project.org) was applied to calculate each samples' immune and stromal scores, respectively, which was performed by R software (4.0.2) with the help of relevant R packages: "estimate," "limma" and "utils." To validate our results [8], we explored the OncoLnc website (http://www.oncolnc.org/), GEPIA platform (http://gepia.cancer-pku.cn/), and the HPA (https://www.proteinatlas.org/) to verify the screened prognostic-genes.…”

Section: Raw Data Collectionmentioning

confidence: 99%

CD247 Functions as a Prognostic Biomarker for Cutaneous Malignant Melanoma Based on the Analysis of Tumor-immune Microenvironment

Chen

Feng

et al. 2020

Preprint

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Background: Cutaneous malignant melanoma (CMM) is among the most lethal cancers. The tumour microenvironment (TME) is closely linked with tumorigenesis, metastasis, and prognosis.Methods: We employed the ESTIMATE algorithm to calculate immune and stromal scores of malignant melanoma tissues from the Cancer Genome Atlas dataset, respectively, and determine core prognosis gene signature examined by COX proportional hazards model. Functional enrichment annotation, the Kyoto Encyclopedia of Genes and Genomes pathways, the Protein-Protein Interaction network, Weighted Gene Co-expression Network Analysis and overall survival analysis were used to formulate potential function of these genes that involved in immune-linked biological processes. CIBERSORT algorithm was used to estimate the abundances of immune cell types in CMM samples. Finally, the OncoLnc platform, the Gene Expression Profiling Interactive Analysis resources, and the Human Protein Atlas database were applied to validate our results. Results：908 differential expressed genes and ten hub genes were screened, and GO annotation indicated that immune response and inflammatory response were firmly involved in CMM tumorigenesis and progression. CD247, identified as the most significant prognostic biomarker, highly expressed in tumor samples and possessed a better prognosis than low expressed samples. The correlation analysis of immune cells infiltration unveiled that CD8+ T cell and Macrophages were intense significant to CMM patients' prognosis. Survival analysis suggested that ten hub genes and infiltrated immune cells are linked to the prognosis of CMM. ConnectiveMap analysis strongly indicated that L-securinine may be a promising candidate medicine for CMM patients.Conclusions: we deeply analyzed the immune-linked genes with the tumour microenvironment, and labeled CD247 as the most intriguing prognostic biomarker for CMM, which may bring better clinical outcomes for CMM patients.

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Identification of novel genes in testicular cancer microenvironment based on ESTIMATE algorithm‐derived immune scores

Cited by 39 publications

References 38 publications

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma

C3AR1 mRNA as a Potential Therapeutic Target Associates With Clinical Outcomes and Tumor Microenvironment in Osteosarcoma

CD247 Functions as a Prognostic Biomarker for Cutaneous Malignant Melanoma Based on the Analysis of Tumor-immune Microenvironment

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