SHOX2 is a Potent Independent Biomarker to Predict Survival of WHO Grade II-III Diffuse Gliomas

Analysis of glutathione Stransferase mu class 5 gene methylation as a prognostic indicator in low-grade gliomas

BACKGROUND

Low-grade gliomas (LGG) are a variety of brain tumors that show different clinical outcomes. The methylation of the GSTM5 gene has been noted in the development of LGG, however, its prognostic importance remains uncertain.

OBJECTIVE

The objective of this study was to examine the correlation between GSTM5 DNA methylation and clinical outcomes in individuals diagnosed with LGG.

METHODS

Analysis of GSTM5 methylation levels in LGG samples was conducted using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The overall survival based on GSTM5 methylation status was evaluated using Kaplan-Meier curves. The DNA methylation heatmap for particular CpG sites in the GSTM5 gene was visualized using the "pheatmap" R package.

RESULTS

The study analyzed that LGG tumors had higher levels of GSTM5 methylation than normal tissues. There was an inverse relationship discovered between GSTM5 expression and methylation. LGG patients with hypermethylation of GSTM5 promoter experienced a positive outcome. Age, grade, and GSTM5 methylation were determined as independent prognostic factors in LGG through both univariate and multivariate Cox regression analyses.

CONCLUSION

Methylation of GSTM5 DNA, specifically at certain CpG sites, is linked to a positive outlook in patients with LGG. Utilizing the "pheatmap" R package to visualize GSTM5 methylation patterns offers important information for identifying prognostic markers and therapeutic targets in low-grade gliomas.

Comprehensive analysis reveals that LTBR is a immune-related biomarker for glioma

Glioma is a common malignant brain tumor with great heterogeneity and huge difference in clinical outcomes. Although lymphotoxin (LT) beta receptor (LTBR) has been linked to immune system and response development for decades, the expression and function in glioma have not been investigated. To confirm the expression profile of LTBR, integrated RNA-seq data from glioma and normal brain tissues were analyzed. Functional enrichment analysis, TMEscore analysis, immune infiltration, the correlation of LTBR with immune checkpoints and ferroptosis, and scRNAseq data analysis in gliomas were in turn performed, which pointed out that LTBR was pertinent to immune functions of macrophages in gliomas. In addition, after being trained and validated in the tissue samples of the integrated dataset, an LTBR DNA methylation-based prediction model succeeded to distinguish gliomas from non-gliomas, as well as the grades of glioma. Moreover, by virtue of the candidate LTBR CpG sites, a prognostic risk-score model was finally constructed to guide the chemotherapy, radiotherapy, and immunotherapy for glioma patients. Taken together, LTBR is closely correlated with immune functions in gliomas, and LTBR DNA methylation could serve as a biomarker for diagnosis and prognosis of gliomas.

Evaluation of DNA methylation levels of SEPT9 and SHOX2 in plasma of patients with head and neck squamous cell carcinoma using droplet digital PCR

Head and neck squamous cell carcinoma (HNSCC) is the seventh most commonly diagnosed cancer globally. HNSCC develops from the mucosa of the oral cavity, pharynx and larynx. Methylation levels of septin 9 (SEPT9) and short stature homeobox 2 (SHOX2) genes in circulating cell‑free DNA (ccfDNA) are considered epigenetic biomarkers and have shown predictive value in preliminary reports in HNSCC. Liquid biopsy is a non‑invasive procedure that collects tumor‑derived molecules, including ccfDNA. In the present study, a droplet digital PCR (ddPCR)‑based assay was developed to detect DNA methylation levels of circulating SEPT9 and SHOX2 in the plasma of patients with HNSCC. The assay was first set up using commercial methylated and unmethylated DNA. The dynamic changes in the methylation levels of SEPT9 and SHOX2 were then quantified in 20 patients with HNSCC during follow‑up. The results highlighted: i) The ability of the ddPCR‑based assay to detect very low copies of methylated molecules; ii) the significant decrease in SEPT9 and SHOX2 methylation levels in the plasma of patients with HNSCC at the first time points of follow‑up with respect to T0; iii) a different trend of longitudinally DNA methylation variations in small groups of stratified patients. The absolute and precise quantification of SEPT9 and SHOX2 methylation levels in HNSCC may be useful for studies with translational potential.

Multiomics blueprint of PANoptosis in deciphering immune characteristics and prognosis stratification of glioma patients

BACKGROUND

As the most prevalent primary brain tumor in adults, glioma accounts for the majority of all central nervous system malignant tumors. The concept of PANoptosis is a relatively new, underlining the interconnection and synergy among three distinct pathways: pyroptosis, apoptosis and necroptosis.

METHODS

We performed single-cell annotations of glioma cells and determined crucial signaling pathways through cell chat analysis. Using least absolute shrinkage and selection operator (LASSO) and Cox analyses, we identified a gene set with prognostic values. Our model was validated using independent external cohort. In addition, we employed single-sample gene set enrichment analysis and xCell analyses to describe the detailed profile of infiltrated immune cells and depicted the gene mutation landscape in the two groups.

RESULTS

We identified seven distinct cell clusters in glioma samples, including oligodendrocyte precursor cells (OPCs), myeloid cells, tumor cells, oligodendrocytes, astrocytes, vascular cells and neuronal cells. We found that myeloid cells showed the highest PANoptosis activity. An intense mutual cell communication pattern between the tumor cells and OPCs and oligodendrocytes was observed. Differentially expressed genes between the high-PANoptosis and low-PANoptosis cell groups were obtained, which were enriched to actin cytoskeleton, cell adhesion molecules and gamma R-mediated phagocytosis pathways. We determined a set of five genes of prognostic significance: SAA1, SLPI, DCX, S100A8 and TNR. The prognostic differences between the two groups in the internal and external sets were found to be statistically significant. We found a marked correlation between S100A8 and activated dendritic cell, macrophage, mast cell, myeloid derived suppressor cell and Treg infiltration. Moreover, we have observed a significant increase of PTEN mutation in the high risk (HR) group of glioma patients.

CONCLUSIONS

In the present study, we have constructed a prognostic model that is based on the PANoptosis, and we have demonstrated its significant efficacy in stratifying patients with glioma. This innovative prognostic model offers novel insights into precision immune treatments that could be used to combat this disease and improve patient outcomes, thereby providing a new avenue for personalized treatment options.

Identification of FXYD6 as the novel biomarker for glioma based on differential expression and DNA methylation

OBJECTIVE

As a single-transmembrane protein of the FXYD family, FXYD6 plays different roles under physiological and pathological status, especially in the nervous system. This study aims to identify FXYD6 as a biomarker for glioma, by analyzing its expression and methylation patterns.

METHODS

Using TCGA and GTEx datasets, we analyzed FXYD6 expression in various tissues, confirming its levels in normal brain and different glioma grades via immunoblotting and immunostaining. FXYD6 biological functions were explored through enrichment analysis, and tumor immune infiltration was assessed using ESTIMATE and TIMER algorithms. Pearson correlation analysis probed FXYD6 associations with biological function-related genes. A glioma detection model was developed using FXYD6 methylation data from TCGA and GEO. Consistently, a FXYD6 methylation-based prognostic model was constructed for glioma via LASSO Cox regression.

RESULTS

FXYD6 was observed to be downregulated in GBM and implicated in a range of cellular functions, including synapse formation, cell junctions, immune checkpoint, ferroptosis, EMT, and pyroptosis. Hypermethylation of specific FXYD6 CpG sites in gliomas was identified, which could be used to build a diagnostic model. Additionally, FXYD6 methylation-based prognostic model could serve as an independent factor as well.

CONCLUSIONS

FXYD6 is a promising biomarker for the diagnosis and prognosis of glioma, with its methylation-based prognostic model serving as an independent factor. This highlights its potential in clinical application for glioma management.

SHOX2 promotes prostate cancer proliferation and metastasis through disruption of the Hippo-YAP pathway

The transcription factor SHOX2 gene is critical in regulating gene expression and the development of tumors, but its biological role in prostate cancer (PCa) remains unclear. In this study, we found that SHOX2 expression was significantly raised in PCa tissues and was associated with clinicopathological features as well as disease-free survival (DFS) of PCa patients. Phenotypic tests showed that the absence of SHOX2 inhibited PCa growth and invasion, while SHOX2 overexpression promoted these effects. Mechanistically, SHOX2 was found to activate the transcription of nephronophthisis type 4 (NPHP4), a gene located downstream of SHOX2. Further analysis revealed that SHOX2 could potentially interfere with the Hippo-YAP signaling pathway through NPHP4 activation, facilitating the oncogenic behavior of PCa cells. These findings highlight SHOX2 as an oncogene in PCa and provide a basis for developing potential therapeutic approaches against this disease.

Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma

BACKGROUND

Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation.

METHODS

We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance.

RESULTS

The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength.

CONCLUSION

Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.

A potential immunotherapeutic and prognostic biomarker for multiple tumors including glioma: SHOX2

BACKGROUND

Short stature homeobox 2 (SHOX2) is significant gene in the development and progression of multiple types of tumors. Nonetheless, the biological role of SHOX2 within pan-cancer datasets has not been investigated. Thus, comprehensive bioinformatics analyses of pan-cancer datasets were conducted to explore how SHOX2 regulates tumorigenesis.

METHODS

A variety of tumor datasets and online analytical tools, including SangerBox, TIMER2, LinkedOmic, GEPIA2 and cBioPortal, were applied to explore SHOX2 expression in various tumors. To ascertain the connections between SHOX2 expression and genetic alterations, SHOX2-related genes and tumor immunity, the pan-cancer datasets were examined. In vitro assays were applied to verify the biological functions of SHOX2 in glioma cells via CCK-8, wound healing, Transwell and colony formation assays.

RESULTS

Analyses found that SHOX2 was overexpressed in multiple cancer types. SHOX2 expression level was significantly correlated with isocitrate dehydrogenase (IDH), 1p/19q, O⁶-methylguanine DNA methyltransferase (MGMT) status and new types of glioma patients. High mRNA expression levels of SHOX2 were associated with a poor prognosis in multiple tumor patients. KEGG enrichment analysis showed that SHOX2-related genes were associated with cell cycle and DNA damage repair. Genetic alterations of SHOX2 were identified in multiple types of cancers, including duplications and deep mutations. Immune analysis showed that SHOX2 was closely correlated with the tumor mutation burden (TMB), microsatellite instability (MSI), neoantigen and neoantigens and immune checkpoint (ICP) in a variety of tumors and could influence the immunotherapy sensitivity of cancers. CCK-8, wound healing, Transwell and colony formation experiments showed that SHOX2 knockdown inhibited glioma cell proliferation, migration, invasion and colony formation abilities.

CONCLUSION

SHOX2 was overexpressed in multiple cancer types in TCGA cohort. SHOX2 knockdown inhibited glioma cell proliferation, migration and colony formation ability. Our study showed that SHOX2 may be an immunotherapeutic and promising prognostic biomarker in certain types of tumors.

System analysis based on the migration- and invasion-related gene sets identifies the infiltration-related genes of glioma

The current database has no information on the infiltration of glioma samples. Here, we assessed the glioma samples' infiltration in The Cancer Gene Atlas (TCGA) through the single-sample Gene Set Enrichment Analysis (ssGSEA) with migration and invasion gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) and the differentially expressed genes (DEGs) were used to identify the genes most associated with infiltration. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the major biological processes and pathways. Protein-protein interaction (PPI) network analysis and the least absolute shrinkage and selection operator (LASSO) were used to screen the key genes. Furthermore, the nomograms and receiver operating characteristic (ROC) curve were used to evaluate the prognostic and predictive accuracy of this clinical model in patients in TCGA and the Chinese Glioma Genome Atlas (CGGA). The results showed that turquoise was selected as the hub module, and with the intersection of DEGs, we screened 104 common genes. Through LASSO regression, TIMP1, EMP3, IGFBP2, and the other nine genes were screened mostly in correlation with infiltration and prognosis. EMP3 was selected to be verified in vitro. These findings could help researchers better understand the infiltration of gliomas and provide novel therapeutic targets for the treatment of gliomas.

Molecular subtypes based on centrosome-related genes can predict prognosis and therapeutic responsiveness in patients with low-grade gliomas

Background

Abnormalities in centrosome regulatory genes can induce chromosome instability, cell differentiation errors, and tumorigenesis. However, a limited number of comprehensive analyses of centrosome-related genes have been performed in low-grade gliomas (LGG).

Methods

LGG data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The ConsensusClusterPlus” R package was used for unsupervised clustering. We constructed a centrosome-related genes (CRGs) signature using a random forest model, lasso Cox model, and multivariate Cox model, and quantified the centrosome-related risk score (centS). The prognostic prediction efficacy of centS was evaluated using a Receiver Operating Characteristic (ROC) curve. Immune cell infiltration and genomic mutational landscapes were evaluated using the ESTIMATE algorithm, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm, and “maftools” R package, respectively. Differences in clinical features, isocitrate dehydrogenase (IDH) mutation, 1p19q codeletion, O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation, and response to antitumor therapy between the high- and low-centS groups were explored. “pRRophetic” R packages were used for temozolomide (TMZ) sensitivity analysis. qRT-PCR verified the differential expression of the centrosomal gene team, the core of which is CEP135, between LGG cells and normal cells.

Results

Two distinct CRG-based clusters were identified using consensus unsupervised clustering analysis. The prognosis, biological characteristics, and immune cell infiltration of the two clusters differed significantly. A well-performing centS signature was developed to predict the prognosis of patients with LGG based on 12 potential CRGs. We found that patients in the high-centS group showed poorer prognosis and lower proportion of IDH mutation and 1p19q codeletion compared to those in the low-centS group. Furthermore, patients in the high-centS group showed higher sensitivity to TMZ, higher tumor mutation burden, and immune cell infiltration. Finally, we identified a centrosomal gene team whose core was CEP135, and verified their differential expression between LGG cells and normal glial cells.

Conclusion

Our findings reveal a novel centrosome-related signature for predicting the prognosis and therapeutic responsiveness of patients with LGG. This may be helpful for the accurate clinical treatment of LGG.

A Novel Insight into Paraptosis-Related Classification and Signature in Lower-Grade Gliomas

Lower-grade gliomas (LGG) are the most common intracranial malignancies that readily evolve to high-grade gliomas and increase drug resistance. Paraptosis is defined as a nonapoptotic form of programmed cell death, which is gradually focused on patients with gliomas to develop treatment options. However, the specific role of paraptosis in LGG and its correlation is still vague. In this study, we first establish the novel paraptosis-based prognostic model for LGG patients. The relevant data of LGG patients were acquired from The Cancer Genome Atlas database, and we found that LGG patients could be divided into three different clusters based on paraptosis via consensus cluster analysis. Through least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis, 10-paraptosis-related gene (PRG) signatures (CDK4, TNK2, DSTYK, CDKN3, CCR4, CASP9, HSPA5, RGR, LPAR1, and PDCD6IP) were identified to separate LGG patients into high- and low-risk subgroups successfully. The Kaplan-Meier analysis and time-dependent receiver-operating characteristic showed that the performances of predicting overall survival (OS) were dramatically high. The parallel results were reappeared and verified by using the Chinese Glioma Genome Atlas and Gene Expression Omnibus databases. Independent prognostic analysis and nomogram construction implied that risk scores could be considered the independent factor to predict OS. Enrichment analysis indicated that immune-related biological processes were generally enriched, and different immune statuses were highly infiltrated in high-risk group. We also confirmed the potential relationship of 10-PRG signatures and drug sensitivity of Food and Drug Administration-approved drugs. In summary, our findings provide a novel knowledge of paraptosis status and crucial direction to further explore the role of PRG signatures in LGG.

Immunogenic cell death-related risk signature predicts prognosis and characterizes the tumour microenvironment in lower-grade glioma

Lower-grade glioma (LGG) is a common malignant primary tumour in the central nervous system, and most patients eventually develop highly aggressive gliomas despite comprehensive traditional treatment. Tumour molecular subtypes and prognostic biomarkers play a crucial role in LGG diagnosis and treatment. Therefore, the identification of novel biomarkers in LGG patients is crucial for predicting the prognosis of glioma. Immunogenic cell death (ICD) is defined as regulated cell death that is sufficient to activate the adaptive immune response of immunocompetent hosts. The combination of ICD and immunotherapy might exert a greater and more persistent antitumour effect in gliomas. In our study, we explored the expression, function, and genetic alterations of 34 ICD-related genes. Using 12 ICD-related genes, including IL17RA, IL1R1, EIF2AK3, CD4, PRF1, CXCR3, CD8A, BAX, PDIA3, CASP8, MYD88, and CASP1, we constructed and validated an ICD-related risk signature via least absolute shrinkage and selection operator (LASSO) Cox regression analysis. All the information was obtained from public databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Our results revealed that ICD-high risk groups have a poor prognosis and might be more sensitive to immune checkpoint blockade (ICB) immunotherapy. In addition, ICD-high risk groups were associated with 1p19q noncodeletion, higher WHO grade, wild type IDH, and an immunosuppressive tumour microenvironment. We verified the prognostic value of 12 ICD-related genes in TCGA and CGGA databases. Immunohistochemistry was performed to verify the expression of several ICD-related genes at the protein level. Our study provides a novel and comprehensive perspective to elucidate the underlying mechanisms of LGG prognosis and direction for future individualized cancer immunotherapy.

A novel hypoxia-driven gene signature that can predict the prognosis and drug resistance of gliomas

Hypoxia spontaneously forms in the interior of glioma tissues and regulates the expression of various genes. However, the status of hypoxia-driven genes in glioma tissues is not completely known. In the current study, RNA-seq data of 695 glioma tissues in The Cancer Genome Atlas (TCGA) were set as a discovery cohort and were used to identify hypoxia-driven genes and construct a novel gene signature. The prognostic values of that signature were verified in data from the TCGA and the Chinese Glioma Genome Atlas (CGGA). The expression and diagnostic values of hypoxia-driven genes were analyzed using immunohistochemistry and receiver operator characteristic curves. Finally, the effects of hypoxia-driven genes on temozolomide (TMZ) resistance were analyzed by western blot, CCK-8 and colony formation assay. A total of 169 hypoxia-driven genes were identified, which were associated with a poor outcome in glioma patients. Among them, 22 genes had a degree score ≥10 and 6 genes (WT1, HOXA2, HOXC6, MMP9, SHOX2 and MYOD1) were selected to construct a signature to classify glioma patients into low- or high-risk groups. That signature had a remarkable prognostic value for glioma patients in TCGA and CGGA. The expression of HOXC6, MMP9, SHOX2 and MYOD1 was associated with hypoxia degree in glioma tissues and in recurrent cases, had a remarkable diagnostic value and a significant relationship with disease free survival in glioma patients. Moreover, SHOX2 was highly expressed in glioma tissues with O-6-methylguanine-DNA methyltransferase (MGMT)-unmethylation and temozolomide (TMZ) resistant glioma cell lines, and associated with MGMT expression. Knockdown the expression of SHOX2 significantly reduced the TMZ-resistance induced by hypoxia in glioma cells. Ultimately, we identified six novel hypoxia-driven genes for reliable prognostic prediction in gliomas and found that SHOX2 might be a potential target to overcome the TMZ resistance induced by hypoxia.

CLSPN is a potential biomarker associated with poor prognosis in low-grade gliomas based on a multi-database analysis

BACKGROUND

The oncogene CLSPN, also known as claspin, has regulatory effects in a variety of tumours; however, it is not clear whether CLSPN is a therapeutic target in low-grade gliomas (LGG). In this study, the prognostic value of CLSPN in LGG and its role as an immunotherapeutic target were evaluated.

METHODS

Transcriptome and methylation data for thousands of patients with glioma were collected from various databases, including The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gene Expression Omnibus. Subsequently, a series of bioinformatics methods were used to evaluate the relationships between CLSPN and prognosis, clinical features, methylation status, immune cells, and molecular signaling pathways in LGG.

RESULTS

CLSPN expression levels were positively correlated with major malignant characteristics of LGG, and low expression of CLSPN was associated with a better prognosis. The methylation sites cg04263115 and cg06100291 negatively regulated the expression of CLSPN, and increased methylation levels at these sites were related to a longer survival time in patients with LGG. CLSPN was positively correlated with tumour-infiltrating immune cells and showed high copy number variation in these cells. There was a positive regulatory relationship between CLSPN expression and programmed death-1 (PD-1) and programmed cell death ligand 1 (PD-L1). A gene set enrichment analysis revealed that CLSPN activates a variety of cancer signaling pathways.

CONCLUSION

CLSPN was identified as an independent risk factor for LGG with excellent prognostic value.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma

Background: The prognosis of low-grade glioma (LGG) is different from that of other intracranial tumors. Although many markers of LGG have been established, few are used in clinical practice. M6A methylation significantly affects the biological behavior of LGG tumors. Therefore, establishment of an LGG prognostic model based on m6A methylation regulatory genes is of great interest.

Methods: Data from 495 patients from The Cancer Genome Atlas (TCGA) and 172 patients from the Chinese Glioma Genome Atlas (CGGA) were analyzed. Univariate Cox analysis was used to identify methylation regulatory genes with prognostic significance. LASSO Cox regression was used to identify prognostic genes. Receiver operating characteristic (ROC) and Kaplan–Meier curves were used to verify the accuracy of the model. Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify cellular pathways that were significantly associated with the prognosis of LGG.

Results: A glioma prognostic model based on five methylation regulatory genes was established. Compared with low-risk patients, patients identified as high risk had a poorer prognosis. There was a high degree of consistency between the internal training and internal validation CGGA cohorts and the external validation TCGA cohort. Furthermore, KEGG and GSEA analyses showed that the focal adhesion and cell cycle pathways were significantly upregulated in high-risk patients. This signature could be used to distinguish among patients with different immune checkpoint gene expression levels, which may inform immune checkpoint inhibitor (ICI) immunotherapy.

Conclusion: We comprehensively evaluated m6A methylation regulatory genes in LGG and constructed a prognostic model based on m6A methylation, which may improve prognostic prediction for patients with LGG.

Methylation and Expression of the Exercise-Related TLR1 Gene Is Associated With Low Grade Glioma Prognosis and Outcome

Background: Exercise improves function, reduces disability, maintains independence, and improves quality of life for low-grade glioma (LGG) patients. Exercise can also improve the effectiveness of cancer treatment. The goal of this research was to find potential exercise related genes that may be used to predict exercise levels and may be used as a biomarker for cancer outcomes.

Methods: The GSE111551 database was thoroughly examined in this research, and the resulting conclusion of exercise-related genes was reached. The protein interaction network (PPI) was used to examine the differentially expressed genes (DEGs). Then the exercise-related gene TLR1 was chosen. The expression, methylation degree, prognosis, and immune relevance of TLR1 were investigated using bioinformatics. In addition, we verified the role of TLR1 in Glioma cell lines.

Results: LGG patients with reduced TLR1 expression and hypermethylation had a better overall survival (OS) and progression free survival (PFS), using the TCGA database. Low TLR1 expression and hypermethylation of TLR1 were found to be independent biomarkers for OS using Cox regression. Furthermore, the CGGA database was used to confirm the prognostic function of TLR1 in this cancer. Finally, most methylation sites of TLR1 were strongly correlated with immune infiltration and immune checkpoint. Then, reducing TLR1 expression substantially slowed the cell cycle and decreased LGG cell proliferation, emigration, and infiltration in vitro.

Conclusions: Exercise-related gene TLR1 has the potential to be a useful prognostic biomarker, and it is thought to be involved in immune cell infiltration and immunotherapy in LGG.

SHOX2 cooperates with STAT3 to promote breast cancer metastasis through the transcriptional activation of WASF3

BACKGROUND

Metastasis is most often the root cause of cancer-related death. Human short stature homeobox 2 (SHOX2), a homeodomain transcription factor, is a novel inducer of epithelial-to-mesenchymal transition in breast cancer cells, though its exact role and underlying mechanisms in metastasis are not well understood.

METHODS

TCGA analysis was performed to identify the clinical relevance of SHOX2 in breast cancer. Gene depletion was achieved by short hairpin RNA and small interfering RNA. Molecular regulations and alterations were assessed by Western blotting, immunoprecipitation, immunohistochemistry, qRT-PCR, chromatin immunoprecipitation coupled with qPCR (ChIP-qPCR), and ChIP/re-ChIP. The impact of SHOX2 signaling on tumor growth and metastasis was evaluated in orthotopic breast tumor mice.

RESULTS

The expression level of SHOX2 is strongly associated with poor distant metastasis-free survival in breast cancer patients and inactivation of SHOX2 suppresses breast tumor growth and metastasis in mice. In breast cancer cells, SHOX2 directly activates Wiskott-Aldridge syndrome protein family member 3 (WASF3), a metastasis-promoting gene, at the transcriptional level, leading to a significant increase in metastatic potential. Mechanistically, SHOX2 activates signal transducer and activator of transcription 3 (STAT3) and recruits it to the WASF3 promoter, where STAT3 cooperates with SHOX2 to form a functional immunocomplex to promote WASF3 transcriptional activity in breast cancer cells. WASF3 knockdown abrogates SHOX2-induced metastasis, but not SHOX2-dependent tumorigenesis.

CONCLUSIONS

These findings provide a critical link between the SHOX2-STAT3-WASF3 signaling axis and metastasis and suggest that the targeting of this signaling node may represent a valuable alternative strategy for combating breast cancer metastasis.

Significantly high expression of NUP37 leads to poor prognosis of glioma patients by promoting the proliferation of glioma cells

Background

The carcinogenic effect of NUP37 has been reported recently in a variety of tumors, but its research in the field of glioma has not been paid attention. The main purpose of this study is to reveal the relationship between NUP37 and prognosis or clinical characteristics of glioma patients.

Methods

First, as a retrospective study, this study included thousands of tissue samples based on a variety of public databases and clinicopathological tissues. Second, a series of bioinformatics analysis methods were used to analyze the NUP37 and glioma samples from multiple databases such as the CGGA, TCGA, GEO, HPA, and GEPIA. Third, to analyze the relationship between the expression level of NUP37 in tumor tissues and cells and a variety of clinical prognostic molecular characteristics, whether it can be an independent risk factor leading to poor prognosis in glioma and whether it has clinical diagnostic value; GSEA was used to analyze the cancer‐related signaling pathways that may be activated by high expression of NUP37. Fifth, CMap was used to analyze small molecule drugs that may inhibit NUP37 expression. Finally, the meta‐analysis of thousands of tissue samples from seven datasets and cell proliferation and migration experiments confirmed that NUP37 has a malignant effect on glioma.

Results

NUP37 is highly expressed in glioma patient tissues and glioma cells, significantly correlates with reduced overall survival, and may serve as an independent prognostic factor with some diagnostic value. Silencing NUP37 suppresses malignant biological behaviors of glioma cells. 4 small molecule drugs that had potential targeting inhibitory effects on NUP37 overexpression.

Conclusions

This study demonstrates for the first time a malignant role of NUP37 in glioma and provides a vision to unravel the complex pathological mechanisms of glioma and a potentially valuable biomarker for implementing individualized diagnosis and treatment of glioma.

Analysis of the Prognostic Value and Gene Expression Mechanism of SHOX2 in Lung Adenocarcinoma

Background: Detection of SHOX2 methylation has been used to assist in the early diagnosis of lung cancer in many hospitals as SHOX2 may be important in the tumorigenesis of lung cancer. However, there are few studies on the mRNA expression, methylation, and molecular mechanism of SHOX2 in lung cancer. We aimed to explore the role of SHOX2 in lung adenocarcinoma (LUAD).

Methods: First, we examined the differential expression of SHOX2 mRNA and methylation in cancerous and normal tissues using databases. Second, we analyzed the relationship between SHOX2 expression and common clinical parameters in LUAD patients. Third, we further explored the methylated level and its specific location of SHOX2 and the mainly factors of SHOX2 gene expression. Finally, we screened the correlatively expressed genes to analyze the pathways from the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes using DAVID.

Results: We found that the mRNA expression of SHOX2 was higher in multiple cancers, including LUAD and lung squamous cell carcinoma (LUSC), than in normal tissues. Among LUAD patients, SHOX2 expression was higher in patients of middle–young age, with smoking history, in advanced stages, and with nodal distant metastasis. In addition, our results showed that patients with high expression of SHOX2 are prone to recurrence, poor differentiation, and poor prognosis. Thus, we identified that SHOX2 might be an oncogene for LUAD progression. The main factor influencing the high expression of SHOX2 mRNA may be DNA methylation, followed by copy number variation (CNV), but not by gene mutations in LUAD. Unexpectedly, we found that SHOX2 undergoes hypomethylation in the gene body instead of hypermethylation in the promoter. Additionally, SHOX2 has cross talk in the PI3K–Akt signaling pathway and ECM–receptor interaction.

Conclusion:SHOX2 is highly expressed in most cancers. SHOX2 gene expression might be mainly regulated by methylation of its gene body in LUAD, and its high expression or hypomethylation indicates poor differentiation and poor prognosis. SHOX2 could be involved in PI3K–Akt and other important cancer-related signaling pathways to promote tumorigenesis.

Identification of Glioma Specific Genes as Diagnostic and Prognostic Markers for Glioma

Background:

Malignant gliomas are the most prevalent malignancy of the brain. However, there was still lack of sensitive and accurate biomarkers for gliomas.

Objective:

To explore the mechanisms underlying glioma progression and identify novel diagnostic and prognostic markers for glioma.

Methods:

By analyzing TCGA dataset, whole-genome genes expression levels were evaluated in 19 different types of human cancers. A protein-protein interacting network was constructed to reveal the potential roles of these glioma special genes. KEGG and GO analysis revealed the potential effect of these genes.

Results:

We identified 698 gliomas specially expressed genes by analyzing TCGA dataset. A protein-protein interacting network was constructed to reveal the potential roles of these glioma special genes. KEGG and GO analysis showed gliomas specially expressed genes were involved in regulating neuroactive ligand-receptor interaction, retrograde endocannabinoid signaling, Glutamatergic synapse, chemical synaptic transmission, nervous system development, central nervous system development, and learning. Of note, GRIA1, GNAO1, GRIN1, CACNA1A, CAMK2A, and SYP were identified to be down-regulated and associated with poor prognosis in gliomas.

Conclusion:

GRIA1, GNAO1, GRIN1, CACNA1A, CAMK2A, and SYP were identified to be down-regulated and associated with poor prognosis in gliomas. We thought this study will provide novel biomarkers for gliomas.

A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

AIM

Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions.

METHODS

survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score.

RESULTS

We identified a total of 1489 genes significantly correlated with patients' prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls.

CONCLUSIONS

The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

Prognostic significance of ARL9 and its methylation in low-grade glioma

This study aimed to determine the value of ARL9 expression or methylation as a biomarker for LGG survival. We investigated the expression, methylation, prognosis and immune significance of ARL9 through bioinformatics analysis. ARL9 is negatively regulated by ARL9 methylation, leading to its low expression in LGG tissues. Both low ARL9 expression and hypermethylation predicted favorable OS and PFS in LGG patients, according to the TCGA database. Cox regression demonstrated that low ARL9 expression and ARL9 hypermethylation were independent biomarkers for OS. Moreover, three other glioma databases were utilized to verify the prognostic role of ARL9 in LGG, and the similar results were reached. A meta-analysis revealed that low ARL9 expression was closely relevant to better OS. Finally, ARL9 expression exhibited a close correlation with some immune cells, especially CD8+ T cells. ARL9 could constitute a promising prognostic biomarker, and probably plays an important role in immune cell infiltration in LGG.

•

This is the first study to report the clinical and prognostic significance of ARL9, a methylation-driven gene，in LGG.

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Meta-analysis could be used for bioinformatics analysis to assess the overall effect of the gene from different datasets.

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ARL9 probably plays a role in the infiltration of immune cells, and acts as a promising prognostic marker in LGG patients.

Long non-coding RNA LINC00460 promotes proliferation and inhibits apoptosis of cervical cancer cells by targeting microRNA-503-5p

Long non-coding RNAs are associated with the pathogenesis of cancers. Moreover, LINC00460 is involved in the development of multiple cancers. However, the function of LINC00460 in cervical cancer (CC) remains inconclusive. Herein, CC tissues and tumor-adjacent tissues were collected from patients. The effect of LINC00460 silencing in cell proliferation and apoptosis in CC was explored in vitro and in vivo. Additionally, the interaction between LINC00460 and miR-503-5p was analyzed using dual luciferase reporter assay. The expression of genes and proteins was assayed using quantitative real-time PCR, western blotting and immunohistochemistry, cell viability using MTT assay, cell cycle distribution using flow cytometry, cell apoptosis using Annexin V staining, Hoechst staining and TUNEL assay. LINC00460 levels in CC tissues were higher than tumor-adjacent tissues. LINC00460 silencing suppressed proliferation and promoted apoptosis of CC cells as evidenced by decreased cell viability, inhibited proliferation-related protein and cell cycle protein expressions and G1/S transition, increased apoptotic cells and Hoechst-positive cells, and enhanced apoptosis-related protein expressions. LINC00460 could bind to miR-503-5p and LINC00460 silencing enhanced miR-503-5p expression and inhibited its target gene expressions in CC cells. MiR-503-5p inhibition reversed LINC00460 silencing-caused inhibition of cell proliferation and miR-503-5p target gene expressions, and promotion of cell apoptosis. LINC00460 silencing also attenuated tumor growth, promoted miR-503-5p levels and cell apoptosis, and inhibited cell proliferation and miR-503-5p target gene expressions in tumor tissues. Hence, LINC00460 functioned as an oncogene in CC that affected cell proliferation and apoptosis via sponging miR-503-5p. This study provides a novel therapeutic target for CC.

OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma

Glioma is the most frequent primary brain tumor that causes high mortality and morbidity with poor prognosis. There are four grades of gliomas, I to IV, among which grade II and III are low-grade glioma (LGG). Although less aggressive, LGG almost universally progresses to high-grade glioma and eventual causes death if lacking of intervention. Current LGG treatment mainly depends on surgical resection followed by radiotherapy and chemotherapy, but the survival rates of LGG patients are low. Therefore, it is necessary to use prognostic biomarkers to classify patients into subgroups with different risks and guide clinical managements. Using gene expression profiling and long-term follow-up data, we established an Online consensus Survival analysis tool for LGG named OSlgg. OSlgg is comprised of 720 LGG cases from two independent cohorts. To evaluate the prognostic potency of genes, OSlgg employs the Kaplan-Meier plot with hazard ratio and p value to assess the prognostic significance of genes of interest. The reliability of OSlgg was verified by analyzing 86 previously published prognostic biomarkers of LGG. Using OSlgg, we discovered two novel potential prognostic biomarkers (CD302 and FABP5) of LGG, and patients with the elevated expression of either CD302 or FABP5 present the unfavorable survival outcome. These two genes may be novel risk predictors for LGG patients after further validation. OSlgg is public and free to the users at http://bioinfo.henu.edu.cn/LGG/LGGList.jsp.

Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma

Background

To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features.

Methods

mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed.

Results

In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P < 0.05). The time-dependent ROC for the R2 subtype was 0.78 for survival at 12 months, 0.76 for survival at 24 months, and 0.76 for survival at 36 months. In the TCIA-LGG dataset, 41 (23.7%) R1 subtype, 40 (23.1%) R2 subtype, 19 (11.0%) R3 subtype and 45 (26.0%) R4 subtype cases were identified. Multivariate analysis revealed that enhancing margin (ill-defined, OR: 9.985; P = 0.003) and T1 + C/T2 mismatch (yes, OR: 0.091; P = 0.023) were associated with the R1 subtype (AUC: 0.708). The average accuracy of the ten-fold cross validation was 71%. Proportion of contrast-enhanced (CE) tumour (> 5%, OR: 14.733; P < 0.001) and necrosis/cystic changes (yes, OR: 0.252; P = 0.009) were associated with the R2 subtype (AUC: 0.832). The average accuracy of the ten-fold cross validation was 82%. Haemorrhage (yes, OR: 8.55; P < 0.001) was positively associated with the R3 subtype (AUC: 0.689). The average accuracy of the ten-fold cross validation was 87%. Proportion of CE tumour (> 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful.

Conclusions

Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes.

Long noncoding RNA FTX is associated with prognosis of glioma patients

Background

Long noncoding RNAs play influential roles in the progression of many types of human malignancies. The present study aimed to explore the prognostic value of long noncoding RNA FTX (FTX) on patients with glioma.

Methods

FTX expression in glioma specimens and matched adjacent non‐neoplasm specimens was examined by a quantitative real‐time polymerase chain reaction assay. Furthermore, assays of the relationships between FTX expression and clinicopathologic characteristics of patients with glioma were also performed. Kaplan–Meier methods were applied for the assays of the overall survival (OS) and progression‐free survival (PFS) of patients and Cox regression assays were used to analyze the clinical value of FTX used as a possible biomarker.

Results

FTX levels were significantly up‐regulated in glioma specimens compared to the paired non‐neoplasm specimens (p < 0.01). Furthermore, high FTX expression in neoplasm tissues was dramatically associated with World Health Organization grade (p = 0.001) and Karnofsky Performance Score (p = 0.009). Kaplan–Meier assays with 187 patients revealed that patients with high level of FTX expression displayed poorer OS (p = 0.002) and PFS (p = 0.000). Subsequently, multivariable Cox regression analysis identified FTX expression as an independent prognostic factor of unfavorable survivals in glioma (OS: p = 0.001; PFS: p = 0.002).

Conclusions

These findings indicated that FTX may be a novel predictor for prognostic assessment of glioma patients. However, studies conducted with larger numbers of patients are essential to confirm our findings.

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma

Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.

Prediction of survival risks with adjusted gene expression through risk-gene networks

MOTIVATION

Network-based analysis of biomedical data has been extensively studied over the last decades. As a successful application, gene networks have been used to illustrate interactions among genes and explain the associated phenotypes. However, the gene network approaches have not been actively applied for survival analysis, which is one of the main interests of biomedical research. In addition, a few previous studies using gene networks for survival analysis construct networks mainly from prior knowledge, such as pathways, regulations and gene sets, while the performance considerably depends on the selection of prior knowledge.

RESULTS

In this paper, we propose a data-driven construction method for survival risk-gene networks as well as a survival risk prediction method using the network structure. The proposed method constructs risk-gene networks with survival-associated genes using penalized regression. Then, gene expression indices are hierarchically adjusted through the networks to reduce the variance intrinsic in datasets. By illustrating risk-gene structure, the proposed method is expected to provide an intuition for the relationship between genes and survival risks. The risk-gene network is applied to a low grade glioma dataset, and produces a hypothesis of the relationship between genetic biomarkers of low and high grade glioma. Moreover, with multiple datasets, we demonstrate that the proposed method shows superior prediction performance compared to other conventional methods.

AVAILABILITY AND IMPLEMENTATION

The R package of risk-gene networks is freely available in the web at http://cdal.korea.ac.kr/NetDA/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

miR‑378a‑3p inhibits cellular proliferation and migration in glioblastoma multiforme by targeting tetraspanin 17

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor and patients with this disease tend to have poor clinical outcome. MicroRNAs (miRs) are important regulators of a number of key pathways implicated in tumor pathogenesis. Recently, the expression of miR‑378 was shown to be dysregulated in several different types of cancer, including gastric cancer, colorectal cancer and oral carcinoma. Additional studies have demonstrated that miR‑378 may serve as a potential therapeutic target against human breast cancer. However, the underlying mechanisms and potential targets of miR‑378a‑3p involved in GBM remain unknown. The aim of the present of was to determine the effects of miR‑378a‑3p and its potential targets. Tetraspanin 17 (TSPAN17) is involved in the neoplastic events in GBM and is a member of the tetraspanin family of proteins. The tetraspanins are involved in the regulation of cell growth, migration and invasion of several different types of cancer cell lines, and may potentially act as an oncogene associated with GBM pathology. The results of the present study showed that high miR‑378a‑3p and low TSPAN17 expression levels were associated with improved survival in patients with GBM. Additionally, high levels of TSPAN17 were linked to the poor prognosis of patients with GBM aged 50‑60, larger tumor sizes (≥5 cm) and an advanced World Health Organization stage. TSPAN17 was identified and confirmed as a direct target of miR‑378a‑3p using a luciferase reporter assay in human glioma cell lines. Overexpression of miR‑378a‑3p in either of U87MG or MT‑330 cells decreased the expression of TSPAN17, promoted apoptosis and decreased proliferation, migration and invasion. Overexpression of TSPAN17 attenuated the aforementioned effects induced by miR‑378a‑3p overexpression. The present study indicated that miR‑378a‑3p suppresses the progression of GBM by reducing TSPAN17 expression, and may thus serve as a potential therapeutic target for treating patients with GBM.

The mSHOX2 is capable of assessing the therapeutic effect and predicting the prognosis of stage IV lung cancer

Background

Instant monitoring of the therapeutic effect of systematic therapy in late-stage lung cancer is crucial for response assessment and strategy adjustment. Previous study found that specific plasma methylation markers may be applied to therapeutic effect assessment. In order to investigate the performance of plasma mSHOX2 in assessing the therapeutic effect and predicting the prognosis of stage IV lung cancer, we performed the study focusing on patients underwent chemotherapy or tyrosine kinase inhibitor (TKI)-based targeted therapy.

Methods

Blood samples from 163 subjects, including 30 stage I, 29 stage II, 26 stage III and 68 stage IV lung cancer patients, were recruited in this study. Quantitative relationship between primary tumor size and the plasma mSHOX2 level was established. Blood samples before therapy and two cycles after therapy were obtained from 68 stage IV patients, and the mSHOX2 level was quantified as ΔΔCt.

Results

Sharp decrease of plasma mSHOX2 level was seen in patients with partial response (PR) while not in those with stable disease (SD). The plasma mSHOX2 level change reflected the degree of response and correlated with the maximal diameter of primary tumors in linear relationship. The mSHOX2 levels before and two cycles after therapy were predictors of the overall survival, while the mSHOX2 level change or the tumor size change were not predictors of the overall survival. Furthermore, univariable and multivariable Cox regression revealed that mSHOX2 level before therapy was the only independent predictor of the overall survival with a hazard ratio of 1.414.

Conclusions

mSHOX2 is effective for therapeutic effect assessment and prognosis prediction of stage IV lung cancer patients underwent systematic therapy.

Analysis of DNA methylation in cancer: location revisited

Changes in DNA methylation in cancer have been heralded as promising targets for the development of powerful diagnostic, prognostic, and predictive biomarkers. Despite the existence of more than 14,000 scientific publications describing DNA methylation-based biomarkers and their clinical associations in cancer, only 14 of these biomarkers have been translated into a commercially available clinical test. Methodological and experimental obstacles are both major causes of this disparity, but the genomic location of a DNA methylation-based biomarker is an intrinsic and essential property that also has an important and often overlooked role. Here, we examine the importance of the location of DNA methylation for the development of cancer biomarkers, and take a detailed look at the genomic location and other relevant characteristics of the various biomarkers with commercially available tests. We also emphasize the value of publicly available databases for the development of DNA methylation-based biomarkers and the importance of accurate reporting of the full methodological details of research findings.

Cell-Free SHOX2 DNA Methylation in Blood as a Molecular Staging Parameter for Risk Stratification in Renal Cell Carcinoma Patients: A Prospective Observational Cohort Study

BACKGROUND

Novel targeted treatments and immunotherapies have substantially changed therapeutic options for advanced and metastatic renal cell carcinomas (RCCs). However, accurate diagnostic tests for the identification of high-risk patients are urgently needed. Here, we analyzed SHOX2 mRNA expression in RCC tissues and SHOX2 gene body methylation quantitatively in circulating cell-free DNA (ccfDNA) and RCC tissues with regard to risk stratification.

METHODS

The clinical performance of SHOX2 methylation was tested retrospectively and prospectively in a training and testing cohort of RCC tissue samples (n = 760 in total). SHOX2 mRNA expression analysis was included in the training cohort. In matched blood plasma samples from the testing cohort (n = 100), we prospectively examined the capability of pretherapeutic quantitative SHOX2 ccfDNA methylation to assess disease stage and identify patients at high risk of death.

RESULTS

SHOX2 gene body methylation was positively correlated with mRNA expression in RCC tissues (training cohort: Spearman ρ = 0.23, P &lt; 0.001). SHOX2 methylation in tissue and plasma strongly correlated with an advanced disease stage (training cohort: ρ = 0.28, P &lt; 0.001; testing cohort/tissue: ρ = 0.40, P &lt; 0.001; testing cohort/plasma: ρ = 0.34, P = 0.001) and risk of death after initial partial or radical nephrectomy [training cohort: hazard ratio (HR) = 1.40 (95% CI, 1.24–1.57), P &lt; 0.001; testing cohort/tissue: HR = 1.16 (95% CI, 1.07–1.27), P = 0.001; testing cohort/plasma: HR = 1.50 (95% CI, 1.29–1.74), P &lt; 0.001].

CONCLUSIONS

Pretherapeutic SHOX2 ccfDNA methylation testing allows for the identification of RCC patients at high risk of death after nephrectomy. These patients might benefit from an adjuvant treatment or early initiation of a palliative treatment.

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

BACKGROUND

Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

METHODS

In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.

RESULTS

We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.

CONCLUSION

In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.