N6-methyladenosine (m6A) modification is involved in tumorigenesis and progression as well as closely correlated with stem cell differentiation and pluripotency. Moreover, tumor progression includes the acquisition of stemness characteristics and accumulating loss of differentiation phenotype. Therefore, we integrated m6A modification and stemness indicator mRNAsi to classify patients and predict prognosis for LGG.We performed consensus clustering, weighted gene co-expression network analysis, and least absolute shrinkage and selection operator Cox regression analysis to identify an m6A regulation- and mRNAsi-related prognostic index (MRMRPI). Based on this prognostic index, we also explored the differences in immune microenvironments between high- and low-risk populations. Next, immunotherapy responses were also predicted. Moreover, single-cell RNA sequencing data was further used to verify the expression of these genes in MRMRPI. At last, the tumor-promoting and tumor-associated macrophage polarization roles of TIMP1 in LGG were validated by in vitro experiments.Ten genes (DGCR10, CYP2E1, CSMD3, HOXB3, CABP4, AVIL, PTCRA, TIMP1, CLEC18A, and SAMD9) were identified to construct the MRMRPI, which was able to successfully classify patients into high- and low-risk group. Significant differences in prognosis, immune microenvironment, and immunotherapy responses were found between distinct groups. A nomogram integrating the MRMRPI and other prognostic factors were also developed to accurately predict prognosis. Moreover, in vitro experiments illustrated that inhibition of TIMP1 could inhibit the proliferation, migration, and invasion of LGG cells and also inhibit the polarization of tumor-associated macrophages.These findings provide novel insights into understanding the interactions of m6A methylation regulation and tumor stemness on LGG development and contribute to guiding more precise immunotherapy strategies.
Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.
Abstract Background: Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. Methods: We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assay were used to measure the cell migration and invasion capacity. Results: We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the epithelial-mesenchymal transition (EMT) process of glioma. Finally, taking COL5A2 as a further research object, the results showed that knockdown of COL5A2 significantly inhibited the migration and invasion of SHG44 and A172 cells. Conclusions: In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.
Abstract Background: Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. Methods: We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assay were used to measure the cell migration and invasion capacity. Results: We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the epithelial-mesenchymal transition (EMT) process of glioma. Finally, taking COL5A2 as a further research object, the results showed that knockdown of COL5A2 significantly inhibited the migration and invasion of SHG44 and A172 cells. Conclusions: In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.
Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12–15 months, and the five-year survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival‐related genes were found among these DEGs (p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2 and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high‐risk group had significantly poorer survival results than those in the low‐risk group. Moreover, with the assistance of GEPIA (http://gepia.cancerpku.cn), all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.
This study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM).Patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM.For the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set.The ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM.
Abstract Background Glioma is the most prevalent malignant tumor that originates from central nervous system. Neddylation, a post-translational modification similar to ubiquitination, is involved in tumorigenesis and progression. However, there were limited studies focused on the neddylation in glioma. Therefore, we aimed to explore the potential role of neddylation in glioma. Methods In this study, neddylation-related genes (NRGs) were identified and were used to construct a prognostic signature for glioma patients. Based on this prognostic index, we also explored the differences in clinical features, mutational landscape, immune cell infiltration between high-risk and low-risk groups. Next, single-cell RNA sequencing analysis was further performed to verify the expression of these genes in NRG signature. At last, one gene selected from the NRG signature were validated by in vitro experiments. Results Seven genes (TOP2A, F2R, UST, HSPA1B, LGALS3BP, UROS, and OSBPL11) were identified to construct the NRG signature, which was able to successfully classify glioma patients into high-risk and low-risk groups. A nomogram based on the NRG signature and other prognostic factors were developed to accurately predict the prognosis of glioma. Significant differences in prognosis, mutational landscape, immune cell infiltration were found between distinct groups. Moreover, in vitro experiments illustrated that knockdown of HSPA1B could inhibit the proliferation, migration, and invasion of glioma cells and also inhibit the polarization of M2 macrophages. Conclusion These findings provide new insights into understanding the relationship between NRGs and glioma development and identify novel biomarkers may help to guiding precise treatments to glioma.
The emergence of vaccine-associated paralytic poliomyelitis has become an ongoing burden of poliomyelitis. During this special period from OPV to IPV-only immunization schedule, we did a meta-analysis to compare the immunogenicity of sequential IPV and OPV versus IPV alone in healthy infants.This systematic review and meta-analysis was registered at international prospective register of systematic reviews (PROSPERO), and the number was CRD42017054889. We performed it as described.Finally, 6 articles were qualified for our review. The results showed that seroconversion rates against all 3 serotype polioviruses were non-inferior and Geometric mean antibody titers (GMTs) were superior in sequential schedules compared with IPV-only schedule. Thus, the sequential vaccination schedules could induce a stronger immunogenicity.To decrease vaccine-associated and vaccine-derived poliomyelitis, it is a reasonable option to select sequential schedules during this special transition from OPV to IPV-only immunization schedule, which coincides with the current WHO recommendations.
Abstract Background Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. Methods We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers. Results We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells. Conclusions In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.
Introduction: Glioblastoma multiforme (GBM) is the most common deadly brain malignancy and lacks effective therapies. Immunotherapy acts as a promising novel strategy, but not for all GBM patients. Therefore, classifying these patients into different prognostic groups is urgent for better personalized management. Materials and Methods: The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the fraction of 22 types of immune-infiltrating cells, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune infiltration-related prognostic scoring system (IIRPSS). Additionally, a quantitative predicting survival nomogram was also established based on the immune risk score (IRS) derived from the IIRPSS. Moreover, we also preliminarily explored the differences in the immune microenvironment between different prognostic groups. Results: There was a total of 310 appropriate GBM samples (239 from TCGA and 71 from CGGA) included in further analyses after CIBERSORT filtering and data processing. The IIRPSS consisting of 17 types of immune cell fractions was constructed in TCGA cohort, the patients were successfully classified into different prognostic groups based on their immune risk score (p = 1e-10). What's more, the prognostic performance of the IIRPSS was validated in CGGA cohort (p = 0.005). The nomogram also showed a superior predicting value. (The predicting AUC for 1-, 2-, and 3-year were 0.754, 0.813, and 0.871, respectively). The immune microenvironment analyses reflected a significant immune response and a higher immune checkpoint expression in high-risk immune group. Conclusion: Our study constructed an IIRPSS, which maybe valuable to help clinicians select candidates most likely to benefit from immunological checkpoint inhibitors (ICIs) and laid the foundation for further improving personalized immunotherapy in patients with GBM.
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