Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes

We wished to construct a prognostic model based on ferroptosis-related genes and to simultaneously evaluate the performance of the prognostic model and analyze differences between high-risk and low-risk groups at all levels. The gene-expression profiles and relevant clinical data of patients with non-small-cell lung cancer (NSCLC) were downloaded from public databases. Differentially expressed genes (DEGs) were obtained by analyzing differences between cancer tissues and paracancerous tissues, and common genes between DEGs and ferroptosis-related genes were identified as candidate ferroptosis-related genes. Next, a risk-score model was constructed using univariate Cox analysis and least absolute shrinkage and selection operator (Lasso) analysis. According to the median risk score, samples were divided into high-risk and low-risk groups, and a series of bioinformatics analyses were conducted to verify the predictive ability of the model. Single-sample gene set enrichment analysis (ssGSEA) was used to investigate differences in immune status between high-risk and low-risk groups, and differences in gene mutations between the two groups were investigated. A risk-score model was constructed based on 21 ferroptosis-related genes. A Kaplan–Meier curve and receiver operating characteristic curve showed that the model had good prediction ability. Univariate and multivariate Cox analyses revealed that ferroptosis-related genes associated with the prognosis may be used as independent prognostic factors for the overall survival time of NSCLC patients. The pathways enriched with DEGs in low-risk and high-risk groups were analyzed, and the enriched pathways were correlated significantly with immunosuppressive status.     

死亡相关的LncRNAs在甲状腺癌中的预后价值及预后风险模型构建

目的:探讨铁死亡相关的长链非编码RNAs(LncRNAs)在甲状腺癌中的预后价值并构建预后风险模型。方法:从癌症基因组图谱(TCGA)数据库下载甲状腺癌的转录本数据和临床数据,铁死亡相关的基因数据集是从铁死亡数据库(http://www.zhounan.org/ferrdb/)下载的259个基因集。得到铁死亡相关LncRNAs,与患者临床信息合并后,通过单因素Cox回归分析和Kaplan-Meier生存分析两种方法得到与甲状腺癌预后相关的铁死亡LncRNAs,通过R的survival包构建COX模型以此来建立最佳预后风险模型并予以验证。结果:获得30个铁死亡相关的LncRNAs,多因素cox分析得到10个与甲状腺癌预后相关的铁死亡LncRNAs,包括AL136366.1、AL162231.2、CRNDE、AC004918.3、LINC02471、AC092279.1、AC046143.1、LINC02454、DOCK9-DT、AC008063.1。Kaplan-Meier生存分析表明高风险组预后较差。单因素和多因素Cox分析表明风险评分可以作为独立预后因子。KEGG通路富集分析发现,差异基因可能与嘧啶代谢、核苷酸切除修复、NOTCH_信号通路等通路有关。结论:通过生物信息学方法筛选出10个与甲状腺癌预后的铁死亡相关LncRNAs,并成功构建预后风险模型。     

急性髓系白血病铁死亡相关基因筛选及预后模型的建立

基于急性髓系白血病(Acute Myeloid Leukemia,AML)临床大数据及多组学数据库探讨铁死亡相关基因在AML中的作用,并建立铁死亡基因表达相关预后模型。整合TCGA数据库中151例AML患者和GTEx数据库中337例正常人外周血的临床和转录组数据。将Wilcoxon检验和单因素Cox分析结果取交集,筛选出预后相关差异表达基因(Differential Expression Genes, DEGs),使用Lasso回归建立基因标志物预后模型,利用受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)评价预测价值,Kaplan-Meier法进行生存分析,对AML患者临床数据进行单因素和多因素Cox回归分析,使用差异基因表达分析等方法比较高、低风险患者间的组学差异,最后,利用BeatAML数据库对基因标志物进行验证。将差异基因表达分析和单因素分析结果取交集,得到13个预后相关DEGs。构建了8个基因标志物的预后评分模型,并将患者分为高、低风险两组;ROC曲线分析证实了模型良好的预测性能;生存分析提示高、低风险组患者的生存率具有显著差异;单因素分析显示年龄和风险评分与患者整体生存显著相关,多因素分析显示,年龄和风险评分是独立预后指标。在2个风险组之间筛选出384个DEGs,GO富集分析结果显示,富集的基因大多与中性粒细胞和白细胞的趋化与迁移等免疫相关分子和通路显著相关,KEGG富集通路主要与TNF信号通路、细胞因子与细胞因子受体相互作用相关。BeatAML数据库验证结果显示,5个基因与预后显著相关。铁死亡相关基因在AML中显著表达,且高风险患者预后较差,该研究对AML铁死亡相关潜在生物标志物的发现和应用奠定了一定的基础。     

Ferroptosis-related gene signature as a prognostic marker for lower-grade gliomas

Ferroptosis is a newly discovered form of programmed cell death, which has unique biological effects on metabolism and redox biology. In this study, the prognostic value of ferroptosis-related genes was investigated in lower-grade gliomas (LGG). We downloaded the ferroptosis-related genes from the FerrDb dataset. Univariate Cox and LASSO regression analyses were applied to identify genes correlated with overall survival (OS). Subsequently, 12 ferroptosis-related genes were screened to establish the prognostic signature using stepwise multivariate Cox regression. According to the median value of risk scores, patients were divided into low- and high-risk subgroups. The Kaplan-Meier curves showed the high-risk group had a lower OS. The predictive power of the risk model was validated using the CGGA. Functional analysis revealed that the terms associated with plasma membrane receptor complex, immune response and glutamate metabolic process were primarily related to the risk model. Moreover, we established a nomogram that had a strong forecasting ability for the 1-, 3- and 5-year OS. In addition, we compared the risk scores between different clinical features. We also detected infiltration of macrophages and monocytes in different subgroups. Overall, our study identified the prognostic signature of 12 ferroptosis-related genes, which has the potential to predict the prognosis of LGG.     

Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta‐training, meta‐testing and validation sets. The meta‐training set was used to build risk prediction model, and the other four data sets were employed for validation. By log‐rank test and univariate COX regression analysis as well as LASSO‐COX, AML patients were divided into high‐risk and low‐risk groups based on AML risk score (AMLRS) which was constituted by 10 survival‐related genes. In meta‐training, meta‐testing and validation sets, the patient in the low‐risk group all had a significantly longer OS (overall survival) than those in the high‐risk group (P < .001), and the area under ROC curve (AUC) by time‐dependent ROC was 0.5854‐0.7905 for 1 year, 0.6652‐0.8066 for 3 years and 0.6622‐0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1‐year, 3‐year and 5‐year OS. Finally, we created a web‐based prognostic model to predict the prognosis of AML patients ( https://tcgi.shinyapps.io/amlrs_nomogram/ ).     

肾透明细胞癌患者铁死亡相关基因的预后作用

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)=(0.024 5)×ALOX5表达值+(0.126 0)×CBS表达值+(0.199 5)×CD44表达值+(0.218 3)×CHAC1表达值+(-0.295 9)×HMGCR表达值+(0.036 7)×MT1G表达值+(0.061 4)×SLC7A11表达值+(-0.080 7)×FDFT1表达值+(0.160 3)×PEBP1表达值+(-0.220 5)×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。     

Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients

Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear.Method: Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application.Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score.Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.     

NDRG2 and TLR7 as novel DNA methylation prognostic signatures for acute myelocytic leukemia

Acute myelocytic leukemia (AML) is an aggressive malignant tumor and typically fatal without treatment. Identification and development of novel biomarkers could be beneficial for the diagnosis and prognosis of AML patients. Here, we aimed to identify the accurate DNA methylation prognostic signatures for AML patients. The DNA methylation data of AML patients and corresponding clinical information were retrieved from The Cancer Genome Atlas database. CPG sites that correlates closely with the survival of the AML patients were identified and further combined into CPG sites pairs to screen the survival-related pairs. The prognostic signatures were identified by the C-index and forward search algorithms and validated by the verification group. Finally, the functional enrichment analysis was performed on these CPG sites. As a result, a total of 498 CPG sites associated with the overall survival of AML patients was obtained. A prognostic signature composed of 10 CPG sites pairs was obtained and validated. The functional enrichment analysis showed prognostic genes were mainly enriched in tumor protein processing, cell differentiation, blood leukocyte immunity, and platelet growth factor pathways. In summary, we identified two accurate prognostic methylation signatures (NDRG2 and TLR7), which would be served as a novel therapy target for AML.     

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.     

Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

Background

Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer.

Methodology/Principal Findings

Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008).

Conclusions/Significance

The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.     

The Overexpression of FEN1 and RAD54B May Act as Independent Prognostic Factors of Lung Adenocarcinoma

Synthetic lethality arises when a combination of mutations in two or more genes leads to cell death. However, the prognostic role of concordant overexpression of synthetic lethality genes in protein level rather than a combination of mutations is not clear. In this study, we explore the prognostic role of combined overexpression of paired genes in lung adenocarcinoma. We used immunohistochemical staining to investigate 24 paired genes in 93 lung adenocarcinoma patients and Kaplan-Meier analysis and Cox proportional hazards models to evaluate their prognostic roles. Among 24 paired genes, only FEN1 (Flap endonuclease 1) and RAD54B (RAD54 homolog B) were overexpressed in lung adenocarcinoma patients with poor prognosis. Patients with expression of both FEN1 and RAD54B were prone to have advanced nodal involvement and significantly poor prognosis (HR = 2.35, P = 0.0230). These results suggest that intensive follow up and targeted therapy might improve clinical outcome for patients who show expression of both FEN1 and RAD54B.     

单羧酸转运蛋白基因SNP 与肝细胞肝癌预后的关联研究

目的:探讨单羧酸转运蛋白基因(monocarboxylate transporter,MCT)单核苷酸多态性(single nucleotide polymorphism,SNPs)与肝细胞肝癌(hepatocellular carcinoma,HCC)根治术患者预后的关系。方法:运用Sequenom i PLEX分型技术对830例原发性HCC患者MCT家族(MCT1、MCT2和MCT4)基因上的8个功能性SNP位点进行基因分型,并分析这些SNP与HCC患者预后的相关性。结果:MCT1基因rs1049434位点和MCT2基因rs995343位点基因型与HCC患者总体生存期及无复发生存期均显著相关(P0.05)。携带MCT1 AT基因型或TT基因型的患者死亡及复发风险均显著低于携带AA基因型的患者(HR=0.72;P=0.042或HR=0.64;P=0.002);携带MCT2 CT基因型或TT基因型的患者死亡及复发风险均显著高于携带CC基因型的患者(HR=1.64;P=0.018或HR=1.52;P=0.026)。而且,MCT1基因rs1049434位点和MCT2基因rs995343位点对HCC预后存在显著的累积效应,携带2个危险基因型的患者死亡及复发风险分别是没有危险基因型患者的2.16倍和2.54倍。此外,携带2个危险基因型的HCC患者在术后行TACE辅助治疗后死亡及复发风险均显著降低(P0.05)。结论:MCT1和MCT2基因上的功能性SNP位点有可能作为HCC根治术后预后评估和TACE辅助治疗反应预测的独立标志物。     

Development and validation of stable ferroptosis- and pyroptosis-related signatures in predicting prognosis and immune status in breast cancer

To develop and validate the predictive effects of stable ferroptosis- and pyroptosis-related features on the prognosis and immune status of breast cancer (BC). We retrieved as well as downloaded ferroptosis- and pyroptosis-related genes from the FerrDb and GeneCards databases. The minimum absolute contraction and selection operator (LASSO) algorithm in The Cancer Genome Atlas (TCGA) was used to construct a prognostic classifier combining the above two types of prognostic genes with differential expression, and the Integrated Gene Expression (GEO) dataset was used for validation. Seventeen genes presented a close association with BC prognosis. Thirteen key prognostic genes with prognostic value were considered to construct a new expression signature for classifying patients with BC into high- and low-risk groups. Kaplan–Meier analysis revealed a worse prognosis in the high-risk group. The receiver operating characteristic (ROC) curve and multivariate Cox regression analysis identified its predictive and independent features. Immune profile analysis showed that immunosuppressive cells were upregulated in the high-risk group, and this risk model was related to immunosuppressive molecules. We successfully constructed combined features of ferroptosis and pyroptosis in BC that are closely related to prognosis, clinicopathological and immune features, chemotherapy efficacy and immunosuppressive molecules. However, further experimental studies are required to verify these findings.     

Inflammatory response mediates cross-talk with immune function and reveals clinical features in acute myeloid leukemia

Accumulated genetic mutations are an important cause for the development of acute myeloid leukemia (AML), but abnormal changes in the inflammatory microenvironment also have regulatory effects on AML. Exploring the relationship between inflammatory response and pathological features of AML has implications for clinical diagnosis, treatment and prognosis evaluation. We analyzed the expression variation landscape of inflammatory response-related genes (IRRGs) and calculated an inflammatory response score for each sample using the gene set variation analysis (GSVA) algorithm. The differences in clinical- and immune-related characteristics between high- and low-inflammatory response groups were further analyzed. We found that most IRRGs were highly expressed in AML samples, and patients with high inflammatory response had poor prognosis and were accompanied with highly activated chemokine-, cytokine- and adhesion molecule-related signaling pathways, higher infiltration ratios of monocytes, neutrophils and M2 macrophages, high activity of type I/II interferon (IFN) response, and higher expression of immune checkpoints. We also used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the sensitivity of AML samples with different inflammatory responses to common drugs, and found that AML samples with low inflammatory response were more sensitive to cytarabine, doxorubicin and midostaurin. SubMap algorithm also demonstrated that high-inflammatory response patients are more suitable for anti-PD-1 immunotherapy. Finally, we constructed a prognostic risk score model to predict the overall survival (OS) of AML patients. Patients with higher risk score had significantly shorter OS, which was confirmed in two validation cohorts. The analysis of inflammatory response patterns can help us better understand the differences in tumor microenvironment (TME) of AML patients, and guide clinical medication and prognosis prediction.     

A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.     

Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.