Background: Pancreatic cancer (PAC) is one of the most devastating cancer types with an extremely poor prognosis, characterized by a hypoxic microenvironment and resistance to most therapeutic drugs. Hypoxia has been found to be one of the factors contributing to chemoresistance in PAC, but also a major driver of the formation of the tumor immunosuppressive microenvironment. However, the method to identify the degree of hypoxia in the tumor microenvironment (TME) is incompletely understood. Methods: The mRNA expression profiles and corresponding clinicopathological information of PAC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. To further explore the effect of hypoxia on the prognosis of patients with PAC as well as the tumor immune microenvironment, we established a hypoxia risk model and divided it into high- and low-risk groups in line with the hypoxia risk score. Results: We established a hypoxia risk model according to four hypoxia-related genes, which could be used to demonstrate the immune microenvironment in PAC and predict prognosis. Moreover, the hypoxia risk score can act as an independent prognostic factor in PAC, and a higher hypoxia risk score was correlated with poorer prognosis in patients as well as the immunosuppressive microenvironment of the tumor. Conclusions: In summary, we established and validated a hypoxia risk model that can be considered as an independent prognostic indicator and reflected the immune microenvironment of PAC, suggesting the feasibility of hypoxia-targeted therapy for PAC patients.

Background: Colon adenocarcinoma (COAD) is a gastrointestinal tumor with a high degree of malignancy. Its deterioration process is closely related to the tumor microenvironment, and transcription factors (TF) play a regulatory role in this process. Currently, there is a lack of exploration between the genes related to the COAD tumor microenvironment and the survival prognosis of patients. Models composed of multiple genes usually predict the survival prognosis of patients more accurately than single genes. We can analyze the multigene models that can predict the prognosis of COAD from the current database. Methods: The limma package of the R programming language is used for gene differential expression analysis. Kaplan-Meier curve is used to analyze the relationship between the patient risk score model and survival data. The hazard model is used to analyze the relationship between the risk score and the clinical data of COAD patients. The information of immune genes and immune cells is obtained from IMMPORT database and TIMER database. Receiver operating characteristic (ROC) curve is used to judge the stability of the model. Results: We found 7 immune genes, which can built a risk score model to predict the survival prognosis of COAD. According to univariate and multivariate analysis, the risk score can be used as an independent predictor. The content of some immune microenvironment cells will also increase as the risk score increases. Conclusions: We found 7 immune genes, such as SLC10A2 (solute carrier family 10 member 2), CXCL3 (C-X-C motif chemokine ligand 3), IGHV5-51 (immunoglobulin heavy variable 5-51), INHBA (inhibin subunit beta A), STC1 (stanniocalcin 1), UCN (urocortin), and OXTR (oxytocin receptor), can constitute a model for predicting the prognosis of COAD. They may provide potential therapeutic targets for clinical treatment of COAD.

Purpose: Glioma is the most common primary cranial brain tumor that arises from the cancelation of glial cells (which can be in the brain or spinal cord). It is due to innate genetic risk factors or induced by a carcinogenic environment. If left untreated, the disease has a poor prognosis. Methods: In this study, we downloaded glioma data from TCGA database and GEO (GSE4412). The GSEA database was used to screen tumor microenvironment-related gene sets. Cancer subtypes were classified by GSVA enrichment method. Results: By GSVA enrichment analysis, we obtain three Gliomas cancer subtypes. After further survival prognosis analysis and biological function analysis, we obtained 13 tumor microenvironment gene sets and 14 core genes that affect patients’ survival prognosis, and these genes have the potential to become targets for targeted therapies and disease detection. Conclusion: We screened a total of 13 gene sets through a series of enrichment analyses, statistical and prognostic analyses, etc. Among them, 14 core genes were identified, namely: TOP2A, TPX2, BUB1, AURKB, AURKA, CDK1, BUB1B, CCNA2, CCNB2, CDCA8, CDC20, KIF11, KIF20A and KIF2C.

Background: Glioma is a neurological tumor with a high degree of malignancy and a poor survival prognosis. The development of bioinformatics-related databases has helped us to further analyze the correlation between genes and tumors. In previous studies, through GSVA enrichment analysis, we obtained 17 core genes (TOP2A, KIF20A, CCNB2, AURKA, KIF11, CDK1, BUB1B, CCNA2, BUB1, CDC20, CDCA8, TPX2, KIF2C, POLA2, POLE2, POLA1 and POLE) that affect the survival prognosis of patients, and since TOP2A order is the highest we found that TOP2A is associated with the clinical features of brain gliomas through a series of bioinformatics databases and statistical analyses. After undergoing bioinformatics analysis, in this study, we further explore the correlation between TOP2A and the clinical samples of real-world glioma patients, so as to analyze the relationship between TOP2A expression and the survival prognosis of patients. It is worth mentioning that only malignant gliomas were designed in this study, and benign gliomas were not included in the scope of this study. Materials and methods: Downloaded glioma data from TCGA database. COX regression analysis assessed clinical relevance. Experimental verification by immunohistochemistry. GSEA analyzes relevant biological functions. Results: Focused on the correlation between TOP2A and glioma, and transported 161 glioma tissue wax blocks from the First Affiliated Hospital of Anhui Medical University, analyzed the correlation between gene expression and clinical data in the real world, and confirmed that it was closely related to the survival prognosis of glioma patients. Conclusions: Through bioinformatics analysis and immunohistochemical experiments combined with real-world patient data, it was confirmed that TOP2A expression was closely related to patient prognosis.

Purpose: When glioma is in WHO grade II-III, glioma is of low grade. Gliomas in this period have the opportunity for surgical treatment and can be treated with a range of targeted therapies to enhance patient survival. Methods: In this study, we downloaded low-grade glioma data from TCGA database and GSEA database. Cancer subtypes were classified by GSVA enrichment method.Results: By GSVA enrichment analysis, we obtain three low-grade gliomas (LGG) cancer subtypes. After further survival prognosis analysis and biological function analysis, we obtained 21 tumor microenvironment gene sets and 17 core genes that affect patients' survival prognosis, and these genes have the potential to become targets for targeted therapies and disease detection.Conclusion: We screened a total of 21 gene sets through a series of enrichment analyses, statistical and prognostic analyses, etc. Among them, 17 core genes were identified, namely: TOP2A, KIF20A, CCNB2, AURKA, KIF11, CDK1, BUB1B, CCNA2, BUB1, CDC20, CDCA8, TPX2, KIF2C, POLA2, POLE2, POLA1 and POLE. TOP2A.