Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.     

IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas

Ferroptosis is a new type of programmed cell death that has excellent anti-tumor potential in different tumors. However, the research on ferroptosis in glioma is still incomplete. In this study, we aimed to revealed the relationship between ferroptosis-related genes (FRGs) and glioma. We collected gene expression profiles of glioma patients from the TCGA and CGGA databases. All glioma samples were classified into five subtypes using the R software ConsensusClusterPlus. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between different subtypes and immune status and ferroptosis. Then co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA). A Gene Ontology (GO) analysis was conducted to analyze the potential biological functions of the genes in the modules. Finally, we identified 10 hub genes using the PPI network. The in vitro experiments were used to validate our predictions. We found that the expression level of IRF2 is positively correlated with the grade of glioma. The overexpression of IRF2 could protect glioma cells from ferroptosis and enhance the invasive and migratory abilities. Silence of IRF2 had the opposite effect. In conclusion, we demonstrated a novel ferroptosis-related signature for predicting prognosis, and IRF2 could be a potential biomarker for diagnosis and treatment in glioma.     

Potential mechanisms underlying the promoting effects of 3D collagen scaffold culture on stemness and drug resistance of glioma cells

Background3D collagen scaffold culture is a good tool to study glioma metastasis and recurrence in vitro.MethodsThe effect of 3D collagen culture on the colony formation, the sphere formation, and drug sensitivity of glioma cells was observed by soft-agar colony formation assays, sphere formation assays, and CCK-8 assays, respectively. 3D-glioma-drug genes were identified by previous results and online databases. Gene enrichment and PPI analyses were performed by R software and Metacsape. Hub 3D-glioma-drug genes were screened by STRING and Cytoscape. TCGA and CGGA databases and R software were used to analyze the distribution of hub genes in glioma and their effects on the prognosis. Western Blot was used to verify the effect of 3D collagen culture on the expression of hub genes. miRNAs targeting hub genes were predicted by ENCORI.Results3D collagen scaffold culture promoted colony formation, sphere formation, and drug resistance of glioma cells. There were 77 3D-glioma-drug genes screened, and the pathways enriched in the protein interaction network mainly included responses to stressors, DNA damage and repair, and drug metabolism. Hub 3D-glioma-drug genes were AKT1, ATM, CASP3, CCND1, EGFR, PARP1, and TP53. These genes and predicted miRNAs were expressed differentially in glioma samples and partially affected the prognosis of patients with glioma. These findings suggested these hub genes and miRNAs may play a key role in the effects generated by the 3D culture model and become new markers for glioma diagnosis and treatment.     

A novel DNA damage and repair‐related gene signature to improve predictive capacity of overall survival for patients with gliomas

Gliomas, as the most lethal and malignant brain tumours in adults, remain a major challenge worldwide. DNA damage and repair‐related genes (DDRRGs) appear to play a significant role in gliomas, but the studies of DDRRGs are still insufficient. Herein, we systematically explored and analysed 1547 DDRRGs in 938 glioma samples from TCGA and CGGA datasets. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a 16‐DDRRG signature, characterized by high‐risk and low‐risk patterns. This risk model harbours robust predictive capability for overall survival of glioma patients. We found the high‐risk score is strongly associated with well‐known malignant features of gliomas, such as the mesenchymal subtype, IDH‐wildtype, 1p/19q non‐codeletion and MGMT promoter unmethylated status. In addition, we found that the high‐risk score is also linked with multiple oncogenic pathways and therapeutic resistance. Significantly, we found the high‐risk group has higher enrichment of immunosuppressive cells (M2‐type macrophages, Tregs and MDSCs) and immune inhibition biomarkers (PD‐1, PD‐L1 and CTLA‐4). Lastly, we proved that SMC4, which has the highest positive regression coefficient in our risk model, is strongly linked with malignant progression and TMZ resistance of gliomas in a E2F1‐dependent manner.     

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Background

Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.

Method

Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.

Results

We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21?+?MED10?+?PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC?=?0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n?=?156).

Conclusion

We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

     

Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis

Background

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.

Methods

In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.

Results

Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.

Conclusions

In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

     

Geraniin suppresses tumor cell growth and triggers apoptosis in human glioma via inhibition of STAT3 signaling

Natural phytochemicals are attracting increasing interest as anticancer agents. The aim of this study is to evaluate the therapeutic potential of geraniin, a major ellagitannin extracted from Geranium sibiricum L., in human glioma. Human U87 and LN229 glioma cells were treated with different concentrations of geraniin, and cell viability, apoptosis, and gene expression were assessed. The involvement of STAT3 signaling in the action of geraniin was examined. We found that geraniin treatment for 48 h significantly (P < 0.05) impaired the phosphorylation of STAT3 and reduced the expression of downstream target genes Bcl-xL, Mcl-1, Bcl-2, and cyclin D1. Exposure to geraniin led to a concentration-dependent decline in cell viability and increase in apoptosis in glioma cells, but had no significant impact on the viability of normal human astrocytes. Measurement of caspase-3 activity showed that geraniin-treated U87 and LN229 cells showed a 1.8–2.5-fold higher caspase-3 activity than control cells. Overexpression of constitutively active STAT3 significantly (P < 0.05) reversed geraniin-mediated growth suppression and apoptosis, which was accompanied by restoration of Bcl-xL, Mcl-1, Bcl-2, and cyclin D1 expression. In an xenograft tumor mouse model, geraniin treatment significantly retarded tumor growth and induced apoptosis. Western blot analysis confirmed the suppression of STAT3 phosphorylation in glioma xenograft tumors by geraniin. Taken together, these data suggest that geraniin exerts growth-suppressive and pro-apoptotic effects on glioma cells via inhibition of STAT3 signaling and may have therapeutic benefits in malignant gliomas.     

Aberrant nuclear lamina contributes to the malignancy of human gliomas

Glioma is the most common type of tumor in the central nervous system, accounting for around 80% of all malignant brain tumors. Previous studies showed a significant association between nuclear morphology and the malignant progress of gliomas. By virtue of integrated proteomics and genomics analyses as well as experimental validations, we identify three nuclear lamin genes (LMNA, LMNB1, and LMNB2) that are significantly upregulated in glioma tissues compared with normal brain tissues. We show that elevated expressions of LMNB1, LMNB2, and LMNA in glioma cells are highly associated with the rapid progression of the disease and the knockdown of LMNB1, LMNB2, and LMNA dramatically suppresses glioma progression in both in vitro and in vivo mouse models. Moreover, the repression of glioma cell growth by lamin knockdown is mediated by the pRb-mediated G1-S inhibition. On the contrary, overexpression of lamins in normal human astrocytes dramatically induced nuclear morphological aberrations and accelerated cell growth. Together, our multi-omics-based analysis has revealed a previously unrecognized role of lamin genes in gliomagenesis, providing a strong support for the key link between aberrant tumor nuclear shape and the survival of glioma patients. Based on these findings, lamins are proposed to be potential oncogene targets for therapeutic treatments of brain tumors.     

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets.Method and ResultsIn this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cellsConclusionThis research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.     

A Drosophila Model for EGFR-Ras and PI3K-Dependent Human Glioma

Gliomas, the most common malignant tumors of the nervous system, frequently harbor mutations that activate the epidermal growth factor receptor (EGFR) and phosphatidylinositol-3 kinase (PI3K) signaling pathways. To investigate the genetic basis of this disease, we developed a glioma model in Drosophila. We found that constitutive coactivation of EGFR-Ras and PI3K pathways in Drosophila glia and glial precursors gives rise to neoplastic, invasive glial cells that create transplantable tumor-like growths, mimicking human glioma. Our model represents a robust organotypic and cell-type-specific Drosophila cancer model in which malignant cells are created by mutations in signature genes and pathways thought to be driving forces in a homologous human cancer. Genetic analyses demonstrated that EGFR and PI3K initiate malignant neoplastic transformation via a combinatorial genetic network composed primarily of other pathways commonly mutated or activated in human glioma, including the Tor, Myc, G1 Cyclins-Cdks, and Rb-E2F pathways. This network acts synergistically to coordinately stimulate cell cycle entry and progression, protein translation, and inappropriate cellular growth and migration. In particular, we found that the fly orthologs of CyclinE, Cdc25, and Myc are key rate-limiting genes required for glial neoplasia. Moreover, orthologs of Sin1, Rictor, and Cdk4 are genes required only for abnormal neoplastic glial proliferation but not for glial development. These and other genes within this network may represent important therapeutic targets in human glioma.     

Proteomic screening and identification of microRNA‐128 targets in glioma cells

Brain‐enriched miR‐128 is repressed in glioma cells, and could inhibit the proliferation of gliomas by targeting genes such as E2F3a and BMI1. To identify more targets of miR‐128 in glioblastoma multiforme, the pulse stable isotope labeling with amino acids in cell culture (pSILAC) technique was used to test its impact on whole protein synthesis in T98G glioma cells. We successfully identified 1897 proteins, of which 1459 proteins were quantified. Among them, 133 proteins were downregulated after the overexpression of miR‐128. Through predictions using various bioinformatics tools, 13 candidate target genes were chosen. A luciferase assay validated that 11 of 13 selected genes were potential targets of miR‐128, and a mutagenesis experiment confirmed CBFB, CORO1C, GLTP, HnRNPF, and TROVE2 as the target genes. Moreover, we observed that the expression of CORO1C, TROVE2, and HnRNPF were higher in glioma cell lines compared to normal brain tissues and presented a tendency toward downregulation after overexpression of miR‐128 in T98G cells. Furthermore, we have validated that CORO1C, TROVE2, and HnRNPF could inhibit glioma cell proliferation. In sum, our data showed that the integration of pSILAC and bioinformatics analysis was an efficient method for seeking the targets of miRNAs, and plentiful targets of miR‐128 were screened and laid the foundation for research into the miR‐128 regulation network.     

Identification of immune-related hub genes and construction of an immune-related gene prognostic index for low-grade glioma

Low-grade glioma (LGG) poses significant management challenges and has a dismal prognosis. While immunotherapy has shown significant promise in cancer treatment, its progress in glioma has confronted with challenges. In our study, we aimed to develop an immune-related gene prognostic index (IRGPI) which could be used to evaluate the response and efficacy of LGG patients with immunotherapy. We included a total of 529 LGG samples from TCGA database and 1152 normal brain tissue samples from the GTEx database. Immune-related differentially expressed genes (DEGs) were screened. Then, we used weighted gene co-expression network analysis (WGCNA) to identify immune-related hub genes in LGG patients and performed Cox regression analysis to construct an IRGPI. The median IRGPI was used as the cut-off value to categorize LGG patients into IRGPI-high and low subgroups, and the molecular and immune mechanism in IRGPI-defined subgroups were analysed. Finally, we explored the relationship between IRGPI-defined subgroups and immunotherapy related indicators in patients after immunotherapy. Three genes (RHOA, NFKBIA and CCL3) were selected to construct the IRGPI. In a survival analysis using TCGA cohort as a training set, patients in the IRGPI-low subgroup had a better OS than those in IRGPI-high subgroup, consistent with the results in CGGA cohort. The comprehensive results showed that IRGPI-low subgroup had a more abundant activated immune cell population and lower TIDE score, higher MSI, higher TMB score, lower T cell dysfunction score, more likely benefit from ICIs therapy. IRGPI is a promising biomarker in the field of LGG ICIs therapy to distinguish the prognosis, the molecular and immunological characteristics of patients.