Glioma progression is heavily driven by heterogeneity and a highly immunosuppressive tumor microenvironment (TME). This study aimed to identify a robust long non-coding RNA (lncRNA) prognostic signature and elucidate its role in TME remodeling. An unbiased transcriptome-wide screening of the TCGA and CGGA databases was performed. A prognostic risk signature was constructed using univariate and multivariate Cox regression. Based on multivariate coefficients, the core protective gene SNAI3-AS1 was selected for in vitro validation in U87 and U251 cells. Additionally, a U87-derived conditioned medium (CM) co-culture system was established to investigate the cooperative reinforcement of SNAI3-AS1 overexpression and the ferroptosis inducer erastin on macrophage polarization (THP-1 cell line), assessed via RT-qPCR and ELISA. A 23-lncRNA prognostic signature was established, demonstrating high accuracy in predicting overall survival and correlating positively with immunosuppressive M2 macrophage infiltration. RT-qPCR results revealed that SNAI3-AS1 was significantly downregulated in glioma tissues. While SNAI3-AS1 overexpression did not alter cell proliferation, it profoundly suppressed three-dimensional matrix migration and invasion. Moreover, SNAI3-AS1 overexpression cooperates with erastin to elevate cellular levels of malondialdehyde and Fe2+, thereby promoting ferroptosis. Crucially, the CM co-culture model revealed that SNAI3-AS1 overexpression cooperated with erastin to trigger robust microenvironmental remodeling, repolarizing macrophages toward an antitumor M1 phenotype. This was evidenced by significantly upregulated M1 markers (iNOS, IL-6, TNF-α) and suppressed M2 markers expression (CD206, IL-10). Administration of ferrostatin-1 completely abrogated these polarization shifts, confirming a ferroptosis-dependent mechanism. We established a robust transcriptome-wide prognostic tool and identified SNAI3-AS1 as a specific suppressor of glioma invasion. Furthermore, SNAI3-AS1 cooperates with ferroptosis induction to drive M1 macrophage polarization, offering a promising TME-remodeling therapeutic strategy.

BACKGROUND: Glioma is the most malignant intracranial tumor. Transient receptor potential (TRP) channel family has been found to be involved in malignant progression of many tumors. However, the relationship between TRP channel-related genes (TCRGs) and glioma remains unclear. METHODS: Gene expression profiles and clinical data of 1,475 glioma patients were obtained from TCGA, CGGA, and GEO databases. Prognostic TCRGs were screened and used to classify the patients. Lasso Cox regression analysis was used to construct a risk model, which was validated in external cohorts, and the patients were stratified into high- and low-risk groups. Immune infiltration and functional enrichment analyses were performed to explore the tumor microenvironment in two groups, while drug sensitivity predictions were conducted. Single-cell RNA sequencing data were analyzed to examine the cell type-specific expression of key model genes. Finally, RT-qPCR was performed on paired glioma and adjacent normal tissues to validate the expression of all model genes. RESULTS: Thirty-seven differentially expressed TCRGs were identified in glioma, of which 30 were associated with patient survival. Consensus clustering revealed three molecular subtypes with distinct prognoses, immune infiltration, and pathway enrichment. A 10-gene (TRPM6, PRKCB, CAMK2G, ADCY5, HTR2A, P2RY2, MAPK13, BDKRB1, PLA2G4D, and TRPV3) prognostic model stratified patients into high- and low-risk groups with significantly different overall survival, validated in external cohorts. High-risk patients exhibited higher immune cell infiltration and were predicted to be more sensitive to drugs including 5-Fluorouracil, Dasatinib, Gemcitabine, and Rapamycin, whereas low-risk patients were more sensitive to Vorinostat, Lapatinib, Gefitinib, and Osimertinib. Single-cell RNA sequencing showed that TRPV3 was expressed in exhausted CD8+ T cells, supporting the model’s relevance to tumor immunity and patient prognosis. RT-qPCR verification indicated that all 10 genes in the model were expressed at lower levels in glioma tissues. CONCLUSION: Based on the expression of TCRGs, we conducted the new subtype classification and a prognostic model for glioma, and is expected to provide theoretical basis for the development of new targets.

Intravoxel Incoherent Motion (IVIM) offers valuable insights into tumor microstructural heterogeneity through diffusion and perfusion. However, the relationship between these features and glioma grading, IDH-1 status, and Ki-67 expression remains insufficiently explored, and there is no consensus on the optimal B-value range for IVIM imaging, which may impact the consistency of results. We retrospectively analyzed IVIM data from 73 glioma patients (mean age, 52.83 years ± 12.79 [SD]; 40 male). Histogram features of IVIM parameters, calculated with four B-value ranges (0-800, 0-1200, 0-1500, and 0–2000 s/mm²), were extracted from the tumor parenchyma. These features were compared across different IDH-1 status, tumor grade, and Ki-67 proliferation index. After checking for multicollinearity, parameters with significant differences were further analyzed using logistic regression, receiver operating characteristic analysis, and decision curve analysis to compare their performance. And Bonferroni correction was applied to adjust p-values for multiple comparisons. Independent risk factors of IDH-1 status, tumor grade and Ki-67 level were identified through multivariate logistic regression in B800, B1200, B1500 and B2000 model. ROC analysis revealed that all models showed similar AUCs for IDH-1 status, tumor grade, and Ki-67 level prediction. The B800 model had the highest specificity (0.87) for IDH-1 status prediction, the B1500 model had the highest specificity (0.91) for tumor grade prediction, and the B1200 model exhibited the highest sensitivity (0.94) for Ki-67 level prediction. The B1500 and B 2000 model demonstrates the best net benefit in most threshold ranges for IDH-1 status, tumor grade and Ki-67 level identification. Histogram features of IVIM parameters demonstrated well performance in distinguishing glioma characteristics and the maximum 1500 s/mm2 may be the optimal.

Brain and central nervous system (CNS) cancers are a major cause of cancer-related mortality and morbidity among adolescents and young adults (AYAs) aged 15-39, presenting significant global health challenges despite advances in treatment. This study assesses the global burden and future trends of CNS cancers in AYAs using data from the Global Burden of Disease (GBD) 2021 database. Data on incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2021 were analyzed for 204 countries and territories. Age-standardized rates for incidence (ASIR), mortality (ASMR), and DALYs (ASDR) were calculated, with temporal trends assessed using Joinpoint regression and future projections estimated using the Bayesian Age-Period-Cohort (BAPC) model. Disparities were evaluated using the Socio-Demographic Index (SDI), a composite measure of income, education, and fertility rates. In 2021, global CNS cancer incidence among AYAs was 57,645 cases, with a prevalence of 271,770. The ASIR was 1.92 per 100,000, the ASMR 0.95 per 100,000, and DALYs totaled 1,744,650. High-SDI regions reported higher ASIR but lower ASMR and ASDR. By 2040, case numbers are projected to rise, while age-standardized rates may stabilize or decline. This study highlights significant global disparities in CNS cancer burden, calling for investments in cancer registries, equitable healthcare access, and tailored prevention and treatment strategies.

Micro RNAs (miRs) have been implicated in various tumorigenic processes. Osteosarcoma (OS) is a primary bone malignancy seen in adolescents. However, the mechanism of miRs in OS has not been fully demonstrated yet. Here, miR-134-5p was found to inhibit OS progression and was also expressed at significantly lower levels in OS tissues and cells relative to normal controls. miR-134-5p was found to reduce vasculogenic mimicry, proliferation, invasion, and migration of OS cells, with miR-134-5p knockdown having the opposite effects. Mechanistically, miR-134-5p inhibited expression of the ITGB1/MMP2/PI3K/Akt axis, thus reducing the malignant features of OS cells. In summary, miR-134-5p reduced OS tumorigenesis by modulation of the ITGB1/MMP2/PI3K/Akt axis, suggesting the potential for using miR-134-5p as a target for treating OS.

Several studies have investigated the role of HIF-1α in predicting the prognosis of patients with glioblastoma, yielding contradictory results. Therefore, we performed a meta-analysis to document the correlation between HIF-1α and glioblastoma in individuals diagnosed with glioblastoma. We searched the PubMed, Cochrane Library, EMBASE, and Web of Science by January 25, 2024. Hazard Ratio (HR) was used to evaluate the relationship between HIF-1α and survival outcome, and Odds Ratio (OR) was adopted for tumor features.There was incorporation of nine observational studies with 607 individuals. The total prevalence of HIF-1α (higher than cut-off values) among individuals with glioblastoma was 0.72 (95

OBJECTIVE:This study aims to analyze the global burden of subarachnoid hemorrhage (SAH) among adolescents and young adults (AYAs) aged 15-39 years from 1990 to 2021, highlighting spatial and temporal trends and providing insights for future public health strategies. METHODS:Data were collected from the Global Burden of Disease Study 2021 (GBD 2021), which includes comprehensive evaluations of health conditions and associated risk factors across 204 countries and territories. The focus was on SAH incidence, prevalence, mortality, and disability-adjusted life years (DALYs) among AYAs. The data were segmented by age groups (15-19, 20-24, 25-29, 30-34, 35-39 years) and socio-demographic index (SDI) quintiles. Statistical analyses, including Joinpoint regression and decomposition analysis, were employed to assess temporal trends and the impact of population growth, aging, and epidemiological changes. RESULTS:From 1990 to 2021, the global number of SAH incident cases among AYAs increased by 12.6%, from 109,120 cases in 1990 to 122,822 cases in 2021. Prevalent cases rose by 17.1%, from 1,212,170 cases in 1990 to 1,419,127 cases in 2021. Conversely, the number of deaths decreased by approximately 26.6%, from 30,348 cases in 1990 to 22,266 cases in 2021. Similarly, DALYs decreased by 23.7%, from 1,996,041 cases in 1990 to 1,523,328 cases in 2021. Notably, over these thirty years, the age-standardized rates (ASR) of incidence, prevalence, mortality, and DALYs for the AYA population showed an overall decreasing trend, despite fluctuations in specific periods. The age-standardized mortality rate (ASMR) and age-standardized DALYs (ASR for DALYs) decreased continuously with an average annual percentage change (AAPC) of -2.2% (95% CI: -2.36, -2.04) and -2.02% (95% CI: -2.17, -1.88), respectively. The age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) had an AAPC of -0.8% (95% CI: -0.85, -0.75) and -0.65% (95% CI: -0.66, -0.64), respectively. Particularly, the ASIR showed a continuous decline from 1990 to 2015, followed by a slight increase from 2014 to 2019 (APC: 0.14%, 95% CI: 0.03, 0.25), and accelerated growth from 2019 to 2021 (APC: 1.23%, 95% CI: 0.88, 1.57). The ASPR declined from 1990 to 2019, followed by an increase from 2019 to 2021 (APC: 0.15%, 95% CI: 0.05, 0.25). Regional analysis revealed substantial burdens in the Middle-SDI and Low-Middle-SDI regions, with the Middle-SDI region having the highest incidence, prevalence, mortality, and DALYs. Decomposition analysis indicated that population growth was the primary driver of increased SAH cases, while epidemiological changes contributed significantly to the decline in deaths and DALYs. CONCLUSION:The findings underscore the need for targeted public health interventions, particularly in low and low-middle-SDI regions, to reduce the burden of SAH among AYAs. Improved healthcare resources, enhanced health education, and preventive strategies are crucial. This study provides valuable data to inform future public health policies and resource allocation, emphasizing the importance of addressing the unique challenges faced by AYAs.

Background:Leukocyte transendothelial migration-related genes (LTEMGs) play a crucial role in the immune response and have been extensively studied in various pathological conditions, including inflammation, infection, and cancer. In recent years, increasing attention has been given to understanding the biological mechanisms of LTEMGs in the context of tumor progression and metastasis. The potential function of LTEMGs in cancer progression remains unclear. The aim of this study is to systematically delineate the relationship between LTEMGs and tumor prognosis and immune microenvironment at the pan-cancer level, providing new biomarkers for personalized immunotherapy. Methods:The gene alteration, messenger RNA (mRNA) expression, and prognostic value of LTEMGs in pan-cancer were evaluated using Bulk and single-cell RNA (scRNA) sequence data. The LTEMGs score was calculated by R package "GSVA". The association of LTEMGs score with tumor microenvironment and immunotherapy response were deeply explored. Results:We assessed the mRNA expression of 114 LTEMGs across various cancers, finding significant upregulation in acute myeloid leukemia (LAML) and pancreatic adenocarcinoma (PAAD). Prognostic analysis indicated most LTEMGs were risk factors in low-grade glioma (LGG), PAAD, uveal melanoma (UVM), and LAML. The LTEMGs score, highest in kidney renal clear cell carcinoma (KIRC) and lowest in UVM, was higher in tumor tissues compared to normal tissues in several cancers. The score was a risk factor for overall survival (OS) in LGG, UVM, and others, but protective in KIRC and some others. LTEMGs score correlated positively with Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling, apoptosis, and immune responses. It also correlated with immune and stromal scores, and immune-related pathways. Higher LTEMGs score was linked to greater immune cell infiltration and poorer immunotherapy outcomes. Single-cell analysis revealed higher LTEMGs score in endothelial and monocyte cells, consistent with reduced immunotherapy responsiveness. Conclusions:Our results reveal that LTEMGs are closely associated with tumor microenvironment. Patients with high LTEMGs score might be resistant to immunotherapy.

To explore the mechanism of the Zhenbao pill (ZBP) in treating spinal cord injury (SCI). The TCMSP Database, HERB Database and literature search were used to screen the effective ingredients and targets of ZBP; SCI-related genes were searched in GeneCards, OMIM, PharmGkb, TTD and DrugBank databases; the potential targets of ZBP for treating SCI were predicted and Venn diagrams were drawn, and the "herb-ingredient-target" network was constructed by Cytoscape software. The PPI network was constructed by STRING software, and the core targets were screened by cytoNCA plug-in; GO enrichment and KEGG pathway analysis were performed on the predicted targets using the DAVID Platform, and visualized with the Microbiology Network Platform. The molecular docking between the key ingredients and the core target was carried out by AutoDockVina software. 391 active ingredients and 836 action targets were obtained from ZBP and there are 1557 SCI related genes in 5 disease databases. The top 5 active ingredients were Quercetin, Camptothecin, Kaempferol, Ethyl iso-allocholate, and Ethyl linoleate, and 5 core genes were SRC, CTNNB1, TP53, AKT1, and STAT3. GO enrichment analysis showed that the core targets were involved in 1206 biological processes, 120 cellular components and 160 molecular functions; KEGG enrichment analysis showed that the core targets involved 183 pathways, including PI3K-Akt signaling pathway and other signaling pathways. Molecular docking indicated that CTNNB1, SRC, TP53, AKT1 and STAT3 showed good binding ability with the active ingredients quercetin, kaempferol and ethyl isobutyric acid. ZBP improves SCI through multi-components, multi-targets and multi-pathways.

Abstract Background: Alzheimer's disease (AD) is the most common neurodegenerative disease. Pyroptosis is a new type of programmed cell death, which can lead to the progression of various diseases. The aim of this study was to explore the role of pyroptosis-related genes (PRGs) in Alzheimer's disease and to build the predictive model. Methods: The expression of PRGs in AD was analyzed based on the GSE33000 dataset, and molecular clustering and immune microenvironment analysis were performed on 310 patient samples. The WGCNA algorithm was used to identify the genes that were specifically expressed between different clusters, and then four machine learning models (RF, GLM, SVM and XGB) were used to construct the predictive models for the risk of AD. The prediction capability of the model was verified by nomogram, calibration, decision curve analyses and five external data sets. Results: Multiple PRGs were differentially expressed between AD and normal brain tissue. Based on differentially expressed PRGs, 310 AD patients were divided into two subtypes by consistent clustering. Immune microenvironment analysis showed significant differences in the degree of immune activation among different subtypes. WGCNA algorithm identified the specific genes between AD and normal individuals, Cluster 1 and Cluster 2. The SVM model has the best prediction performance with low residual error and root mean square error, and high area under ROC curve (AUC=0.933). Finally, a prediction model based on five genes (GPR4, STAT3, CASP4, CLIC1 and TNFRSF10B) was constructed and showed satisfactory performance on five externally validated data sets. Nomogram, calibration curve and decision curve analysis proved the prediction performance of the model. Conclusions: This study systematically analyzed the complex relationship between PRGs and AD, and constructed a good prediction model to distinguish AD from normal individuals, which is expected to provide reference for related research.

目的 探讨细胞周期相关基因在胶质瘤患者中的表达及预后价值.方法 利用CGGA数据库筛选与胶质瘤患者预后相关的细胞周期基因,并基于CGGA与TCGA中胶质瘤患者的临床数据,通过LASSO回归分析,构建预测患者生存情况的预后模型.根据计算公式,区分高低风险组患者,组间进行GSEA富集分析与ssGSEA免疫微环境分析.结果 筛选到10个与患者预后密切相关的细胞周期基因,LASSO回归分析纳入4个基因[细胞周期蛋白依赖性激酶抑制剂2C(CDKN2C)、姐妹染色单体分离的PTTG1调控因子(PTTG1)、细胞周期蛋白依赖性激酶2(CDK2)、WEE1 G2检查点激酶(WEE1)]构建预后模型,计算公式为:风险值(risk socre)=(0.008)×CDKN2C表达量+(0.022)×PTTG1表达量+(0.031)×CDK2表达量+(0.127)×WEE1表达量.生存分析显示,高风险组患者生存率低于低风险组,ROC曲线表明,模型在CGGA与TCGA队列中,均具有较好的预测能力.GSEA富集分析显示,高风险组富集到多个细胞周期进程相关的信号通路,提示可能参与胶质瘤的恶性进程.免疫微环境分析表明,高风险组患者的免疫细胞浸润与免疫反应激活程度均高于低风险组.结论 基于细胞周期相关基因的预后模型可较好地应用于胶质瘤患者的预后预测,纳入的关键基因可能是胶质瘤治疗的可靠靶点.

探讨铁死亡相关基因在肾透明细胞癌患者中的表达及其预后价值.通过TCGA数据库下载KIRC的相关测序数据与检索到的铁死亡相关基因取交集,进行铁死亡相关基因的差异分析.之后利用单变量和多变量Cox回归分析,筛选具有预后价值的基因,构建预测患者生存情况的风险评分模型,并对模型进行验证.对高低风险组进行GO与KEGG通路富集,探讨风险差异的可能原因;通过ssGSEA分析,评估高低风险组间的免疫浸润情况.在KIRC患者的肿瘤组织和正常组织中,共得到21个差异的铁死亡相关基因;通过单因素Cox回归分析,获得28个与KIRC预后相关的基因;之后进行Lasso回归与多因素Cox回归分析,结果显示有10个基因被纳入模型,计算公式为:风险值(Risk score)=(0.0245)×ALOX5表达值+(0.1260)×CBS表达值+(0.1995)×CD44表达值+(0.2183)×CHAC1表达值+(-0.2959)×HMGCR表达值+(0.0367)×MT1G表达值+(0.0614)×SLC7A11表达值+(-0.0807)×FDFT1表达值+(0.1603)×PEBP1表达值+(-0.2205)×GOT1表达值.生存状态图表明,高风险组死亡病例数多于低风险组;ROC曲线表明风险评分模型具备一定预测能力;K-M生存分析显示,高风险组总体生存率低于低风险组(P=5.73×10-13).GO与KEGG富集分析提示,高低风险组间免疫情况及IL-17信号通路存在显著差异;进一步的ssGSEA富集显示,高低风险组间大部分免疫细胞的评分存在显著差异.基于铁死亡相关基因的预后风险评分模型可用于KIRC的预后预测,针对铁死亡相关基因设计靶点可能是治疗KIRC的一种新选择.

Objective To investigate the role of period 2 (Per2) protein in the death of cardiomyocytes induced by β1-adrenergic receptor autoantibodies (β1-AA). Methods Sixteen male SD rats aged 6~8 weeks were randomly divided into active immunization (model) group and control group (n=8). The model group was immunized with the second extracellular loop of beta1-adrenoceptor (β1-AR-EC Ⅱ), and the control group was injected with Na2CO3 and other solutions. The rat serum was subsequently collected at 8 weeks, followed by the purification of β1-AA. H9c2 cardiomyocyte were selected and randomly divided into control group, β1-AA group, and β1-AR+β1-AA group. Cell viability of each group was detected by CCK-8 assay (n=8). Then H9c2 cells in the control group and β1-AA 1 μmol/L group were synchronized with dexamethasone for 4 h, the expression of Per2 in cardiomyocytes at different circadian time (CT) points was measured by Western blotting, and JTK_CYCLE was used to analyze the circadian rhythm parameters (n=11). Moreover, Per2 in H9c2 cells was knocked down or overexpressed by lentiviral shPer2 and lentiviral Per2, respectively; RT-PCR and Western blotting were performed to detect the changes of Per2 expression (n=6). On the basis of knockdown (n=8) or overexpression (n=10) of Per2, the H9c2 cells were further treated with β1-AA, and the cell survival rate was tested by CCK-8 assay. Results CCK-8 assay showed that the survival rate of H9c2 cells was significantly decreased after β1-AA treatment (P < 0.05). Western blotting demonstrated that β1-AA remarkably inhibited the rhythmic expression of Per2 protein in the cardiomyocytes (JTK_CYCLE, P>0.05), with the decrease at CT8 and CT16 most obviously (P < 0.01). Knockdown of Per2 expression reduced the survival rate of cardiomyocytes, which was further lowered after β1-AA treatment (P < 0.001). However, overexpression of Per2 notably reversed the decline in H9c2 survival rate induced by β1-AA (P < 0.001). Conclusion Per2 protein inhibits β1-AA induced H9c2 cardiomyocyte death.

Abstract Background: This study aims to identify key molecular targets in Alzheimer’s disease (AD) occurrence and progression.Methods: GSE5281 was obtained by screening and collection of the GEO database and denoted as set 1. Differential analysis was carried out on AD samples and healthy samples in set 1. Hippocampal tissues which were extracted from APP/PS1 double transgenic mice were used to measure the syntaxin 17 (STX17) gene and protein expression. Set 1 samples was divided into the STX17 high expression group and the low expression group and denoted as set 2. Differential analysis was carried out on set 2. The hippocampal sample expression matrix in GSE48350 was named as set 3 for weighted gene co-expression network analysis (WGCNA). GSE33000 was used to construct the Least absolute shrinkage and selection operator (LASSO) model for analysis and validation. Results: 6151 differentially expressed genes (DEGs) were obtained in set 1. STX17 has significantly low expression in the hippocampal tissues of AD mice. 3651 DEGs were obtained from set 2. 2658 common DEGs were obtained in the overlap of the two sets. 22 co-expression modules were obtained from set 3. 401 genes were ultimately obtained from the overlap between genes in the WGCNA significance module and the common DEGs in the two sets and Cytoscape was used for further visualization analysis to obtain the PPI networks of 18 crucial genes. LASSO model construction and fitting was carried out to obtain seven genes common to AD and STX17 (AMPH, GAD2, GAP43, REPS2, SGIP1, STXBP1, SYN2).Conclusion: Bioinformatics analysis was used to examine the intrinsic mechanisms of AD pathogenesis and determine the important role of STX17 in AD progression. Integrated analysis was used to obtain crucial molecular targets common to AD and STX17, which provides new ideas for future AD mechanistic studies and treatment.

多形性胶质母细胞瘤(glioblastoma multiforme，GBM)是恶性程度最高的神经系统肿瘤，中位总生存期为9~15个月。自噬是细胞在相关基因的调控下利用溶酶体降解自身受损的细胞器和大分子的过程，其与恶性肿瘤的发生发展密切相关。研究表明 [1]，自噬相关长链非编码RNA(autophagy related long non-coding RNAs，ATLs)是一类新兴的生物标志物，本研究旨在GBM患者中筛选具备预后价值的ATLs，并构建预后模型。

目的 通过构建肾乳头状细胞癌(PRCC)相关ceRNA网络并进行分析,同时联合免疫细胞浸润,寻找新的PRCC治疗靶点和预后标志物.方法 从癌症基因组图谱(TCGA)数据库下载PRCC相关基因及其临床数据,并利用R软件筛选出差异表达的RNA分子构建ceRNA网络.进行单因素Cox分析、lasso回归分析以及多因素Cox分析筛选出与预后相关的基因并构建基因风险模型.通过CIBERSORT算法对免疫细胞进行评估,去除所有表达量为0的免疫细胞并对其进行单因素Cox分析、lasso回归分析以及多因素Cox分析,筛选出与预后相关的免疫细胞构建免疫细胞风险模型.最后将基因风险模型和免疫细胞风险模型进行共表达分析.结果 经过筛选后,我们发现了 11个重要的基因以及2个重要的免疫细胞,分别为ELN、COL1A1、EFEMP1、SYNGR3、DBT、KCTD15、RNF149、IKBIP、ATAD5、TCF4和 hsa-miR-133a-3p 这 11 个基因,以及巨噬细胞 M0和活化的CD4+记忆T细胞;共表达分析中,发现DBT与巨噬细胞M0(R=0.22,P=0.045)、TCF4与活化的CD4+记忆T细胞(R=0.37,P＜0.001)呈正相关.结论 筛选出的11个基因和2个免疫细胞是PRCC患者新的潜在治疗靶点以及预后标志物,共表达分析发现DBT与巨噬细胞M0、TCF4与活化的CD4+记忆T细胞呈正相关.

构建由自噬相关基因组成的预后模型,预测肝细胞癌(HCC)患者的生存预后情况,为其个性化诊疗和临床研究提供依据.利用TCGA数据库中HCC的测序信息与人类自噬数据库联合,筛选差异表达的自噬相关基因,对其进行GO富集与KEGG通路分析;通过单因素与多因素Cox分析筛选与患者生存预后明显相关的风险基因,构建预后风险评分模型;根据模型计算患者风险值并验证模型,利用GEPIA2.0网页工具与HPA数据库对风险基因在HCC中的表达情况以及与生存预后的关系进行验证.结果发现,HCC肿瘤组织相较正常组织共筛选到61个差异表达的自噬相关基因(表达上调57个,下调4个),GO富集与KEGG通路分析显示均与自噬有关;单因素Cox分析共筛选到12个与患者生存预后相关的基因,多因素Cox分析后共有4个基因被纳入预后风险评分模型,分别是SQSTM1、HDAC1、RHEB和ATIC,计算公式为:风险值(risk score)=SQSTM1表达量×0.185+HDAC1表达量×0.382+RHEB表达量×0.423+ATIC表达量×0.438;K-M生存曲线显示高风险组生存率低于低风险组,风险曲线提示4个基因与不良预后密切相关,ROC曲线证明模型具有预测意义;GEPIA2.0网页工具以及HPA数据库表明高表达4个基因均导致患者生存率降低.所构建预后风险评分模型可有效预测HCC患者生存预后情况,并提供个性化诊疗策略.

BACKGROUND: Glioma is the most malignant tumor of the central nervous system, with a poor prognosis. Pyroptosis is known to regulate the malignant phenotype of tumor cells, thus affecting the prognosis of patients. However, the role of pyroptosis-related genes (PRGs) in glioma remains unclear. METHODS: We used the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Rembrandt database of patients with glioma to construct a PRG-based prognostic model and analyzed the relationship between the prognostic model and tumor immune microenvironment. The Wilcox test was used to compare the expression of PRGs in glioma and normal tissues based on TCGA. Univariate Cox and LASSO regression were used to construct the prognostic model. The CGGA and Rembrandt database were used as validation sets to validate the model. RESULTS: Five genes were included in the model (BAX, CASP1, CASP3, CASP6, and NOD1). The survival of patients in the high-risk group was lower than that in the low-risk group. The receiver operating characteristic curve showed that the model had good prognostic evaluation ability and accuracy in all 3 cohorts of patients with glioma. The correlation analysis between the prognostic model and immune infiltration showed that the degree of immune cell infiltration, immune response process, and the expression level of immune checkpoints in the high-risk group were higher than those in the low-risk group. CONCLUSIONS: We have constructed a reliable PRG-related prognostic model, which can provide reference for the prognostic evaluation of patients with glioma.