Assessment of expression of interferon γ (IFN-G) gene and its antisense (IFNG-AS1) in breast cancer

Yaghoobi, Hajar; Azizi, Hakim; Oskooei, Vahid Kholghi; Taheri, Mohammad; Ghafouri‐Fard, Soudeh

doi:10.1186/s12957-018-1508-1

Cited by 21 publications

(11 citation statements)

References 12 publications

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“…Interferon gamma (IFNG) has been known to regulate tumor immune surveillance and tumorgenesis ( Dong et al, 2015 ). Yaghoobi et al (2018) showed that IFNG had a slightly higher expression level in breast cancer tissues compared with their adjacent non-cancerous tissues, but IFNG expression was significantly higher in grade 1 samples compared with grade 2 ones. Tumor protein 63 (TP63), known as a member of the p53 family, maintains stem cell pluripotency and suppresses the metastatic potential of cancer cells by regulating genomic programs ( Abbas et al, 2018 ).…”

Section: Discussionmentioning

confidence: 96%

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

Zhong

Chen

Yang

et al. 2020

PeerJ

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We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

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Section: Discussionmentioning

confidence: 96%

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

Zhong

Chen

Yang

et al. 2020

PeerJ

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“…have evaluated the IFNG and its antisense (IFNG-AS1) roles in breast cancer and have proposed the involvement of IFNG and IFNG-AS1 in the pathogenesis of breast cancer [92]. In another study, Gao et al investigated the role of IFNG pathway in the anti-CTLA resistance mechanism.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

Ataei

Majidi

Zahiri

et al. 2021

Preprint

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Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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“…Zhang et al (2019) identified ALB as a hub gene related to colorectal cancer liver metastasis. It was reported that the expression of IFNG was significantly higher in HER2(−) breast cancer tissue in comprison with HER2(+) tissue (Yaghoobi, Azizi, Oskooei, Taheri, & Ghafouri‐Fard, 2018). Besides this, Karachaliou et al (2018) demonstrated that the progression‐free survival was significantly longer in melanoma or non‐small‐cell lung cancer patients receiving nivolumab with high IFNG expression compared with those with low expression.…”

Section: Discussionmentioning

confidence: 99%

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

Huang

et al. 2020

Journal Cellular Physiology

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Testicular cancer is the most common solid malignancy among young men. We downloaded data of testicular cancer patients from The Cancer Genome Atlas database to find novel genes in the testicular cancer microenviroment based on ESTIMATE algorithm-derived immune scores. A total of 156 cases of testicular cancer were included in this study and 165 cases of normal testicular tissues were used. We divided the testicular cancer patients into high-and low-score groups based on their immune scores. We identified 1,226 differentially expressed genes (fold change > 2, false discovery rate < 0.05), including 688 downregulated genes and 538 upregulated genes, between these two groups. The top Gene Ontology terms were involved in the immune response-regulating cell surface receptor signaling pathway, immune response-activating cell surface receptor signaling pathway, external side of the plasma membrane, and receptor ligand activity. By performing the Kyoto Encyclopedia of Genes and Genomes analysis, we demonstrated that cAMP signaling pathway was highly enriched among these differentially expressed genes. High expression of LINC01564, LINC02208, ODAM, RNA5SP111, and RNU6-196P were found to be associated with poor overall survival. The expression of genes was further validated by the Human Protein Atlas and only ALB and IFNG were demonstrated to be differentially expressed between testis tissue and testicular cancer tissue.

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Assessment of expression of interferon γ (IFN-G) gene and its antisense (IFNG-AS1) in breast cancer

Cited by 21 publications

References 12 publications

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

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

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