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.     

Development of a prognostic model based on an immunogenomic landscape analysis of medulloblastoma

Medulloblastoma (MB) is one of the most common central nervous system tumors in children. At present, the vital role of immune abnormalities has been proved in tumorigenesis and progression. However, the immune mechanism in MB is still poorly understood. In the present study, 51 differentially expressed immune-related genes (DE-IRGs) and 226 survival associated immune-related genes (Sur-IRGs) were screened by an integrated analysis of multi-array. Moreover, the potential pathways were enriched by functional analysis, such as ‘cytokine–cytokine receptor interaction’, ‘Ras signaling pathway’, ‘PI3K-Akt signaling pathway’ and ‘pathways in cancer’. Furthermore, 10 core IRGs were identified from DE-IRGs and Sur-IRGs. And the potential regulatory mechanisms of core IRGs were also explored. Additionally, a new prognostic model, including 7 genes (HDGF, CSK, PNOC, S100A13, RORB, FPR1, and ICAM2) based on IRGs, was established by multivariable COX analysis. In summary, our study revealed the underlying immune mechanism of MB. Moreover, we developed a prognostic model associated with clinical characteristics and could reflect the infiltration of immune cells.     

Identification of a four immune-related genes signature based on an immunogenomic landscape analysis of clear cell renal cell carcinoma

Renal clear cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which has strong immunogenicity. A comprehensive study of the role of immune-related genes (IRGs) in ccRCC is of great significance in finding ccRCC treatment targets and improving patient prognosis. In this study, we comprehensively analyzed the expression of IRGs in ccRCC based on The Cancer Genome Atlas datasets. The mechanism of differentially expressed IRGs in ccRCC was analyzed by bioinformatics. In addition, Cox regression analysis was used to screen prognostic related IRGs from differentially expressed IRGs. We also identified a four IRGs signature consisting of four IRGs (CXCL2, SEMA3G, PDGFD, and UCN) through lasso regression and multivariate Cox regression analysis. Further analysis results showed that the four IRGs signature could effectively predict the prognosis of patients with ccRCC, and its predictive power is independent of other clinical factors. In addition, the correlation analysis of immune cell infiltration showed that this four IRGs signature could effectively reflect the level of immune cell infiltration of ccRCC. We also found that the expression of immune checkpoint genes CTLA-4, LAG3, and PD-1 in the high-risk group was higher than that in the low-risk group. Our research revealed the role of IRGs in ccRCC, and developed a four IRGs signature that could be used to evaluate the prognosis of patients with ccRCC, which will help to develop personalized treatment strategies for patients with ccRCC and improve their prognosis. In addition, these four IRGs may be effective therapeutic targets for ccRCC.     

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.     

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.     

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.     

Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

Objective: To identify immune-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC).Methods: The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to obtain the gene expression profile. Immune-related lncRNAs were screened from the Molecular Signatures Database v4.0 (MsigDB). We performed a survival analysis of critical lncRNAs. Further, the function of prognostic lncRNAs was inferred using the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) to clarify the possible mechanisms underlying their predictive ability. The assessment was performed in clinical samples and PTC cells.Results: We obtained 4 immune-related lncRNAs, 15 microRNAs (miRNAs), and 375 mRNAs as the key mediators in the pathophysiological processes of PTC from the GEO database. Further, Lasso regression analysis identified seven prognostic markers (LINC02550, SLC26A4-AS1, ACVR2B-AS1, {"type":"entrez-nucleotide","attrs":{"text":"AC005479.2","term_id":"4508155","term_text":"AC005479.2"}}AC005479.2, LINC02454, and AL136366.1), most of which were related to tumor development. The KEGG pathway enrichment analysis showed different, changed genes mainly enriched in the cancer-related pathways, PI3K-Akt signaling pathway, and focal adhesion. Only SLC26A4-AS1 had an intersection in the results of the two databases.Conclusion: LncRNA SLC26A4-AS1, which is the most associated with prognosis, may play an oncogenic role in the development of PTC.     

Comprehensive characterization of pathological stage-related genes of papillary thyroid cancer along with survival prediction

It is crucial to understand the differences across papillary thyroid cancer (PTC) stages, so as to provide a basis for individualized treatments. Here, comprehensive function characterization of PTC stage-related genes was performed and a new prognostic signature was developed for advanced patients. Two gene modules were confirmed to be closely associated with PTC stages and further six hub genes were identified that yield excellent diagnostic efficiency between tumour and normal tissues. Genetic alteration analysis indicates that they are much conservative since mutations in the DNA of them rarely occur, but changes of DNA methylation on these six genes show that 12 DNA methylation sites are significantly associated with their corresponding genes' expression. Validation data set testing also suggests that these six stage-related hub genes would be probably potential biomarkers for marking four stages. Subsequently, a 21-mRNA-based prognostic risk model was constructed for PTC stage III/IV patients and it could effectively predict the survival of patients with strong prognostic ability. Functional analysis shows that differential expression genes between high- and low-risk patients would promote the progress of PTC to some extent. Moreover, tumour microenvironment (TME) of high-risk patients may be more conducive to tumour growth by ESTIMATE analysis.     

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

MiR-21靶基因预测和生物信息学分析

为了利用生物信息学方法预测miR-21的靶基因及其功能,为后续研究miR-21及其靶基因在结肠癌发生中的作用机制奠定基础。研究通过miRBase获取并分析多个物种的miR-21的序列特征;应用Target Scan、Pic Tar,miRanda及miRecords 4种在线工具预测miR-21的靶基因,结合已证实的靶基因,对靶基因进行功能注释和信号通路富集分析;通过查找文献,综述miR-21的功能,结合功能注释和信号通路富集分析为进一步研究mir-21在结肠癌发生中的作用提供理论基础。     

Molecular characterization of lung cancer: A two-miRNA prognostic signature based on cancer stem-like cells related genes

Lung cancer is one of the deadliest cancers worldwide. To increase the survival rate of lung cancer, it is necessary to explore specific prognosis markers. More and more evidence finds that noncoding RNA is closely associated with the survival of lung cancer, and cancer stem cells (CSCs) also play a significant role in the progress of lung cancer. The objective of this study is to find CSLCs genes that affect the prognosis of lung cancer. The differential expression of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), messenger RNAs (mRNAs) in the Cancer Genome Atlas (TCGA) database and differential expression data from microarray of CD326⁺ and CD326⁻ A549 cell are intersected to identify stable and consistent expression genes (2 lncRNAs, 15 miRNAs, and 134 mRNAs). The intersection of lncRNAs and miRNAs is analyzed by univariate and multivariate Cox regression to obtained prognostic genes. Two miRNAs (miR-30b-5p and miR-29c-3p) are significantly correlated with the overall survival rate. Then using these two miRNAs to construct a risk score model as a prognosis signature of lung cancer. Subsequently, we analyzed the association between two miRNAs and clinical information of lung cancer patients, of which T stage, Neoplasm cancer and risk score (P < .05) can be used as independent prognostic indicators of lung cancer. Finally, target genes of 2 miRNAs and 134 mRNAs were annotated with Gene Ontology and analyzed with Kyoto Encyclopedia of Genes and Genomes pathway, and verified with the GEO database. In summary, this study illustrates the role of miRNAs in the promotion of lung cancer by CSCs, which is important to find molecular biomarkers of lung cancer.