Erythropoietin as a Potential Prognostic Biomarker Correlated With Immune Infiltration in Hepatocellular Carcinoma

Background. Hepatocellular carcinoma (HCC) is a malignant tumor with poor prognosis. Immune checkpoint genes are considered novel tumor immunotherapy targets for many cancer types. Erythropoietin (EPO) is a protein secreted mainly in the liver and kidneys; in several cancer types, EPO expression stimulates tumor growth and corelates with patient survival. The study aimed to identify EPO as a prognostic biomarker correlated with immune infiltration in HCC. Methods. We investigated the correlation between EPO expression and HCC patient clinical characteristics using data from The Cancer Genome Atlas. The relationship between EPO expression and HCC patient survival was investigated using Kaplan–Meier analysis. We also used R (v.3.6.3) to analyze the correlation between EPO expression and immune infiltration in HCC. Results. We found that EPO expression was downregulated in HCC. Notably, EPO was upregulated in advanced-stage HCC and had a diagnostic value of 0.83 (p < 0.001) in HCC diagnosis. HCC patients with increased EPO expression had poor prognoses. Furthermore, altered EPO expression was associated with immune cell infiltration and immune checkpoint gene expression in HCC. Conclusions. EPO expression was positively correlated with hepatocellular carcinoma stage, and negatively correlated with patient prognosis, with influening HCC immune cell infiltration and checkpoint gene expression. EPO can be as both a potential prognostic biomarker and a novel potential treatment target for HCC.Our work is not a clinical trial, but preclinical basic study.

Signature based on immune-related LncRNA can predict overall survival of osteosarcoma patients

Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma. Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated. Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy. Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma. Keywords: Osteosarcoma, TCGA, LncRNA, Tumor immunology, Prognosis.

Signature Based on Immune-Related LncRNA Can Predict Overall Survival of Osteosarcoma Patients

Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma.Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated.Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy.Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma.

Identification of immune prognostic signature of lung carcinoma based on Genome-wide immune expression profile

Background : Lung cancer is one of the most common types of cancer with low early diagnosis rate and poor prognosis. The integration of immune checkpoint gene expression data and patient prognosis information can help identify the immune subtypes of lung cancer and provide reference for individualized gene immunotherapy in patients with lung cancer. Methods : The data of immune gene expression for lung cancer patients were obtained from TCGA and GEO databases. The relationship between the expressions of 45 immune checkpoint genes (ICGs) and prognosis were analysed. In the other hand, the correlation between the expressions of 45 biomarkers , tumor mutation load (TMB), MMRs, neoantigens and other immunotherapy biomarkers were been identified. Ultimately, prognosis-related ICGs were combined with IDO1, CD274, and CTLA4 to divide lung cancer immune subgroups and the prognostic differences between lung cancer immune subgroups were identified. Results: Based on TCGA database and GEO database, 9 and 11 ICGs were obtained respectively, which were closely related to prognosis. There was a certain synergistic relationship between them. The expression of CD200R1 had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68 and GZMB genes, indicating that it may cause the expression disorder of adaptive immune resistance pathway genes. Based on CD200R1 and combination with IDO1, CD274 and CTLA4, the group with high expression of CD200R1 and low expression of IDO1, CD274 and CTLA4 had the best prognosis among the immune subtypes. Conclusion : Our research provides a method of integrating immune checkpoint gene expression profile and clinical prognosis information to identify immune subtypes of lung cancer, which can provide a unique reference for gene immunotherapy of lung cancer patients.

Topoisomerase II inhibitors induce cGAS-STING dependent inflammation resulting in cytokine induction and immune checkpoint activation

Tumours with genomic instability demonstrate enhanced immunogenicity and potential for response to immune checkpoint blockade (ICB). We previously demonstrated activation of the cGAS-STING pathway following loss of DNA repair, resulting in cytokine induction, lymphocytic infiltration and immune checkpoint activation. Here we explore the role of chemotherapies in inducing this innate immune response, identifying topoisomerase II (topo-II) inhibitors, particularly doxorubicin and epirubicin, as potent inducers of a cGAS-STING dependent interferon response. Mechanistically, topo-II inhibition resulted in significant induction of cytoplasmic DNA and subsequent micronuclei formation, a requirement for efficient cGAS-STING activation and consequent cytokine and immune checkpoint gene induction. Importantly, increased cytokine and immune checkpoint gene expression, as well as increased immune cell infiltration, was also observed in patient derived breast tumour biopsies following topo-II inhibitor-based treatment. Taken together, this study indicates topo-II inhibitors such as doxorubicin, may be best placed to induce immunogenic inflammation, and thereby increase responses to ICB therapies.SignificanceThis work demonstrates how topo-II inhibitors induce STING-pathway activation, cytokine induction and immune checkpoint protein upregulation in cancer cells and provides a rationale for combining topo-II inhibitors with ICB therapy in early breast cancer.