基于TCGA数据库分析甲状腺癌基因表达谱

为分析甲状腺癌基因表达谱,筛选疾病相关的基因标志物。基于肿瘤基因组图谱(TCGA)数据库中的甲状腺癌基因表达数据,运用R/Bioconductor统计平台进行数据处理与统计学分析。分别应用edgeR算法和limma算法选取肿瘤组织与对照组间倍数改变 > 2,P< 0.05的基因作为差异基因;进一步运用Medcalc统计软件进行受试者工作特征曲线(ROC)分析,鉴定出有诊断标志物潜在应用价值的基因标志物。通过两种运算方法筛选出甲状腺癌组织中存在着1 945个差异基因(上调基因1 033个,下调基因912个);根据差异倍数进一步鉴定出11个基因在肿瘤组织中表达上调,且对鉴别肿瘤组与对照组有较好的应用价值。本研究分析了TCGA中的甲状腺癌表达谱数据,鉴定出了与疾病诊断显著相关的差异表达基因,能够为探索疾病发生发展机制及寻找新型分子标志物提供依据。     

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT?qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.     

Do innate killing mechanisms activated by inflammasomes have a role in treating melanoma?

Melanoma, as for many other cancers, undergoes a selection process during progression that limits many innate and adaptive tumor control mechanisms. Immunotherapy with immune checkpoint blockade overcomes one of the escape mechanisms but if the tumor is not eliminated other escape mechanisms evolve that require new approaches for tumor control. Some of the innate mechanisms that have evolved against infections with microorganisms and viruses are proving to be active against cancer cells but require better understanding of how they are activated and what inhibitory mechanisms may need to be targeted. This is particularly so for inflammasomes which have evolved against many different organisms and which recruit a number of cytotoxic mechanisms that remain poorly understood. Equally important is understanding of where these mechanisms will fit into existing treatment strategies and whether existing strategies already involve the innate killing mechanisms.     

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.     

Comprehensive analysis of lncRNA-associated ceRNA network in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies and morbidity and mortality are increasing rapidly. Increasing evidence showed the close correlation between aberrant expression of certain RNAs and the occurrence and development of CRC. However, comprehensive analyses of differentially expressed profiles of linRNA in CRC based on large sample size have been lacking. In the present study, based on RNA-seq data obtained from the TCGA (The Cancer Genome Atlas) database, we identified 1176 lncRNAs, 245 miRNAs and 2083 mRNAs whichaberrantly expressed in the colorectal cancer tissues compared with the adjacent non-tumorous tissues. A Kaplan-Meier curve analysis was used to study the overall survival rate of the three RNA-related CRC patients. After constructing the ceRNA network, we performed the KEGG enrichment pathway analysis on ceRNA-related differentially expressed mRNAs and found that these mRNAs were remarkably enriched in the pathways associated with CRC. Combining the differentially expressed lncRNAs with clinical pathological variables of CRC patients, we also found that LINC00400 and LINC00355 not only contribute to the regulation of ceRNA network, but also show significantchanges in its expression in multiple CRC pathological stages, indicating that LINC00400 and LINC00355 can be considered as promising therapeutic targets for CRC.     

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.