Protein disulphide isomerase can predict the clinical prognostic value and contribute to malignant progression in gliomas

Increasing evidence from structural and functional studies has indicated that protein disulphide isomerase (PDI) has a critical role in the proliferation, survival and metastasis of several types of cancer. However, the molecular mechanisms through which PDI contributes to glioma remain unclear. Here, we aimed to investigate whether the differential expression of 17 PDI family members was closely related to the different clinicopathological features in gliomas from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas data sets. Additionally, four subgroups of gliomas (cluster 1/2/3/4) were identified based on consensus clustering of the PDI gene family. These findings not only demonstrated that a poorer prognosis, higher WHO grade, lower frequency of isocitrate dehydrogenase mutation and higher 1p/19q non-codeletion status were significantly correlated with cluster 4 compared with the other clusters, but also indicated that the malignant progression of glioma was closely correlated with the expression of PDI family members. Moreover, we also constructed an independent prognostic marker that can predict the clinicopathological features of gliomas. Overall, the results indicated that PDI family members may serve as possible diagnostic markers in gliomas.     

Correlation of zinc finger protein 2, a prognostic biomarker,with immune infiltrates in liver cancer

Purpose: The expression and clinical value of zinc finger protein 2 gene (ZIC2) in hepatocellular carcinoma (HCC) were analyzed by mining gene information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.Methods: Gene chip data sets were retrieved from GEO and TCGA and screened for differentially expressed genes in HCC. Gene expression profile interaction analysis (GEPIA) and Kaplan–Meier curves were used to analyze the relationship between differentially expressed genes (DEGs) and survival and prognosis in patients with HCC. Moreover, the Genecards database was used to extract ZIC2-related proteins and to analyze the physiological process of protein enrichment. Furthermore, the relationships between ZIC2 gene and tumor cell immune invasion and that between immune cell infiltration and the 5-year survival rate were studied using the tumor immune evaluation resource (TIMER) database.Results: Datasets from GEO and TCGA revealed that ZIC2 was differentially expressed in HCC tissues and normal tissues (P<0.05). High ZIC2 expression was associated with overall survival (OS) and progress-free survival in HCC patients. Overall, 25 ZIC2 related proteins, including Gli3, PRKDC, and rnf180 were identified and protein enrichment analysis indicated these were associated with four types of cell components, six types of cell functions, and eight types of biological processes. ZIC2 was positively correlated with immune infiltration cells in patients with HCC, and higher expression of ZIC2 mRNA CD4+T cells is associated with a better 5-year survival.Conclusion: ZIC2 gene may be used as an immune response marker in liver cancer to predict the prognosis of HCC.     

Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas

The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein–protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8⁺ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.     

Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17-IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.     

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.     

Expression and prognostic characteristics of m5C regulators in low-grade glioma

Glioma is the most common intracranial malignant tumour. A clear diagnosis and molecular targeted therapy are of great significance for improving the survival time and quality of life of patients with low-grade glioma. 5-methylcytosine methylation is one of the ways of RNA modification, but there are limited studies on the role of m⁵C methylation of low-grade glioma. Single-nucleotide variant, RNA expression matrix and corresponding clinical data of low-grade glioma came from public database. The single-nucleotide variant and expression of m⁵C regulators were estimated. A prognostic model based on m⁵C regulators was constructed by Cox regression. Potential functions of these molecules were assessed by gene set enrichment analysis. DNMT3A mutation was the most frequent among the m⁵C regulators in low-grade glioma. NSUN3, TET2, TRDMT1, ALYREF, DNMT3B, DNMT1, NOP2 and NSUN2 were up-regulated. One prognostic model was constructed which had a strong predictive power for the overall survival of low-grade glioma. We studied the expression and prognostic characteristics of m⁵C regulators in low-grade glioma, supplied biomarkers for the diagnosis and prognosis and provided the foundation for the study of the pathogenesis of low-grade glioma.     

结合自噬相关基因表达谱和临床因素的肺腺癌预后预测模型

对肺腺癌自噬相关基因进行生物信息学分析,结合多基因预后标志和临床参数构建能够预测肺腺癌患者预后的模型。首先,对TCGA肺腺癌数据中的938个自噬相关基因进行差异分析,获得了82个差异自噬相关基因,使用单因素Cox比例风险回归模型从差异自噬相关基因中筛选出候选基因,通过 lasso回归进一步筛选出预后相关基因,分别是ARNTL2、NAPSA、ATG9B、CAPN12、MAP1LC3C和KRT81。通过多因素Cox回归分析以构建风险评分模型,根据最优cutoff值将患者分为高低风险组,生存曲线显示高低风险组之间生存差异显著,ROC曲线显示风险评分的预测能力良好,并在内、外验证集中得到验证。同时对传统的临床因素进行单因素和多因素Cox回归分析,结果显示Stage、复发和风险评分能够独立预测预后,结合这三个独立的预后参数以构建列线图模型,使用一致性指数、校准曲线评估列线图的预测能力,结果显示预测结果与实际结果之间具有良好的一致性。通过与Stage和风险评分的比较发现,列线图的预测能力表现最佳。基于肺腺癌相关的自噬基因和临床参数构建了一个列线图模型来预测肺腺癌患者的预后生存,这可能为临床医生提供了一种可靠的预后评估工具。     

高等植物赤霉素2-氧化酶基因的克隆、表达及其调控

赤霉素(GA)是一类重要的植物激素,对高等植物整个生命周期的生长发育起关键作用。调控赤霉素生物合成和代谢途径中的关键酶基因的表达可以控制植物体内赤霉素的含量。GA2-氧化酶是调节赤霉素合成和代谢的关键酶之一,使活性GA失活。本文主要对GA2-氧化酶基因的克隆、表达调控及其在植物基因工程中的应用等方面进行综述,为通过基因工程技术调控植物体内活性赤霉素的含量从而得到改良品种提供思路。     

Variants in Notch signalling pathway genes,PSEN1 and MAML2, predict overall survival in Chinese patients with epithelial ovarian cancer

To identify genetic variants in Notch signalling pathway genes that may predict survival of Han Chinese patients with epithelial ovarian cancer (EOC), we analysed a total of 1273 single nucleotide polymorphisms (SNPs) within 75 Notch genes in 480 patients from a published EOC genomewide association study (GWAS). We found that PSEN1 rs165934 and MAML2 rs76032516 were associated with overall survival (OS) of patients by multivariate Cox proportional hazards regression analysis. Specifically, the PSEN1 rs165934 AA genotype was associated with a poorer survival (adjusted hazards ratio [adjHR] = 1.41, 95% CI = 1.07‐1.84, and P = .014), compared with the CC + CA genotype, while MAML2 rs76032516 AA + AC genotypes were associated with a poorer survival (adjHR = 1.58, 95% CI = 1.16‐2.14, P = .004), compared with the CC genotype. The combined analysis of these two SNPs revealed that the death risk increased as the number of unfavourable genotypes increased in a dose‐dependent manner (P_trend < .001). Additionally, the expression quantitative trait loci analysis revealed that the SNP rs165932 in the rs165934 LD block (r² = .946) was associated with expression levels of PSEN1, which might be responsible for the observed association with SNP rs165934. The associations of PSEN1 rs165934 and MAML2 rs76032516 of the Notch signalling pathway genes with OS in Chinese EOC patients are novel findings, which need to be validated in other large and independent studies.