Development and validation of LRP1B mutation-associated prognostic model for hepatocellular carcinoma

Purpose: To develop a lipoprotein receptor-related protein 1B (LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.     

Identification of a risk prediction model for clinical prognosis in HER2 positive breast cancer patients

BackgroundNew biomarkers are needed to identify different clinical outcomes for HER2+ breast cancer (BC).MethodsDifferential genes of HER2+ BC were screened based on TCGA database. We used WGCNA to identify the genes related to the survival. Genetic Algorithm was used to structure risk prediction model. The prognostic model was validated in GSE data.ResultsWe constructed a risk prediction model of 6 genes to identify prognosis of HER2+ BC, including CLEC9A, PLD4, PIM1, PTK2B, AKNAD1 and C15orf27. Kaplan-Meier curve showed that the model effectively distinguished the survival of HER2+ BC patients. The multivariate Cox regression suggested that the risk model was an independent predictor for HER2+ BC. Analysis related to immune showed that significant differences in immune infiltration between high- and low-risk groups classified by the prognostic model.ConclusionsOur study identified a risk prediction model of 6 genes that could distinguish the prognosis of HER2+ BC.     

ALYREF associated with immune infiltration is a prognostic biomarker in hepatocellular carcinoma

BackgroundAlthough ALYREF has been demonstrated to have a role in a number of malignancies, its role in hepatocellular carcinoma (HCC) has received little attention. Our objective was to research at the prognostic value, biological role and relevance of ALYREF to the immune system in HCC.MethodsThe expression of ALYREF and its relationship with clinical parameters of HCC patients were analyzed by liver cancer cohort (LIHC) of The Cancer Genome Atlas. The expression and prognosis were verified by immunohistochemistry experiments. Gene transfection, CCK-8, scratch healing, transwell invasion and flow cytometry were used to assess the molecular function of ALYREF in vitro. The TIMER and TISIDB online data portals were used to assess the relevance of ALYREF to immunization. Stepwise regression analysis of ALYREF-related immune genes in the LIHC training set was used to construct a prognostic risk prediction model. Also, construct a nomogram to predict patient survival. The testing set for internal verification.ResultsKnockdown of ALYREF changed the biological phenotypes of HCC cells, such as proliferation, apoptosis, and invasion. In addition, the expression of ALYREF in HCC affected the level of immune cell infiltration and correlated with the overall survival time of patients. The constructed immune prognostic model allows for a valid assessment of patients.ConclusionALYREF is increased in HCC, has an impact on cellular function and the immune system, and might be used as a prognostic marker.     

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

BackgroundRecent studies have emphasized the close relationship between macrophages and tumor immunity, and the prognosis of lung adenocarcinoma (LUAD) patients is intimately linked to this. Nonetheless, the prognostic signature and classification of different immune patterns in LUAD patients based on the macrophages is largely unexplored.MethodsTwo sc-RNAseq datasets of LUAD patients were collected and reprocessed. The differentially expressed genes (DEGs) related to macrophages between LUAD tissues and normal lung tissues were then identified. Based upon the above genes, three distinct immune patterns in the TCGA-LUAD cohort were identified. The ssGSEA and CIBERSORT were applied for immune profiling and characterization of different subtypes. A four-gene prognostic signature for LUAD patients was established based on the DEGs between the subtypes using stepwise multi-Cox regression. TCGA-LUAD cohort was used as training set. Five GEO-LUAD datasets and an independent cohort containing 112 LUAD samples were used for validation. TIDE (tumor immune dysfunction and exclusion) and drug sensitivity analyses were also performed.ResultsMacrophage-related differentially expressed genes were found out using the publicly available scRNA-seq data of LUAD. Three different immune patterns which were proved to have distinct immune infiltration characteristics in the TCGA-LUAD cohort were recognized based on the above macrophage-related genes. Thereafter, 174 DEGs among the above three different immune patterns were figured out; on the basis of this, a four-gene prognostic signature was constructed. This signature distinguished the prognosis of LUAD patients well in various GSE datasets as well as our independent cohort. Further analyses revealed that patients which had a higher risk score also accompanied with a lower immune infiltration level and a worse response to several immunotherapy biomarkers.ConclusionThis study highlighted that macrophage were significantly associated with TME diversity and complexity. The four-gene prognostic signature could be used for predicting outcomes and immune landscapes for patients with LUAD.     

Identification of novel prognostic indicators for oral squamous cell carcinoma based on proteomics and metabolomics

BackgroundThe low 5-year survival rate of oral squamous cell carcinoma (OSCC) suggests that new prognostic indicators need to be identified to aid the clinical management of patients.MethodsSaliva samples from OSCC patients and healthy controls were collected for proteomic and metabolomic sequencing. Gene expressed profiling was downloaded from TCGA and GEO databases. After the differential analysis, proteins with a significant impact on the prognosis of OSCC patients were screened. Correlation analysis was performed with metabolites and core proteins were identified. Cox regression analysis was utilized to stratify OSCC samples based on core proteins. The prognostic predictive ability of the core protein was then evaluated. Differences in infiltration of immune cells between the different strata were identified.ResultsThere were 678 differentially expressed proteins (DEPs), 94 intersected DEPs among them by intersecting with differentially expressed genes in TCGA and GSE30784 dataset. Seven core proteins were identified that significantly affected OSCC patient survival and strongly correlated with differential metabolites (R² > 0.8). The samples were divided into high- and low-risk groups according to median risk score. The risk score and core proteins were well prognostic factor in OSCC patients. Genes in high-risk group were enriched in Notch signaling pathway, epithelial mesenchymal transition (EMT), and angiogenesis. Core proteins were strongly associated with the immune status of OSCC patients.ConclusionsThe results established a 7-protein signatures with the hope of early detection and the capacity for risk assessment of OSCC patient prognosis. Further providing more potential targets for the treatment of OSCC.     

Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma

BackgroundImmune cells, vital components of tumor microenvironment, regulate tumor survival and progression. Lung adenocarcinoma (LUAD), the tumor with the highest mortality rate worldwide, reconstitutes tumor immune microenvironment (TIME) to avoid immune destruction. Data have shown that TIME influences LUAD prognosis and predicts immunotherapeutic efficacy. The related information about the role of TIME's characteristics in LUAD is limited.MethodsWe performed unsupervised consensus clustering via machine-learning techniques to identify TIME clusters among 1906 patients and gathered survival data. The characteristics of TIME clusters of LUAD were visualized by multi-omics analysis, pseudo-time dynamic analysis, and enrichment analysis. TIME score model was constructed by principal component analysis. Comprehensive analysis and validation were conducted to test the prognostic efficacy and immunotherapeutic response of TIME score.ResultsTIME clusters (A, B and C) were constructed and exhibited different immune infiltration states. Multi-omics analyses included significant mutated genes (SMG), copy number variation (CNV) and cancer stemness that were significantly different among the three clusters. TIME cluster A had a lower SMG, lower CNV, and lower stemness but a higher immune infiltration level compared to TIME clusters B and C. TIME score showed that patients in low TIME score group had higher overall survival rates, higher immune infiltration level and high expression of immune checkpoints. In validation cohorts, low TIME score subgroup had better drug sensitivity and favorable immunotherapeutic response.ConclusionWe constructed a stable model of LUAD immune microenvironment characteristics that may improve the prognostic accuracy of patients, provide improved explanations of LUAD responses to immunotherapy, and provide new strategies for LUAD treatment.     

Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency.Methods: Lasso regression analysis followed by univariate Cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset {"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520. R package ‘clusterProfiler’ was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by single-sample GSEA (ssGSEA) of R package ‘GSVA’.Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective by combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition.Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.     

Differential expression and bioinformatics analysis of exosome circRNAs in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a fatal malignant tumor with an unfavorable prognosis. Increasing evidence indicated circRNAs were associated with the pathogenesis and progression of tumors, but data on the expression of serum exosomal circRNAs in PDAC are scarce. This study attempted to explore the prognostic value and function of serum exosomes in PDAC patients.MethodsMicroarray-based circRNA expression was determined in PDAC and paired with normal serum samples, and the intersection of differentially expressed circRNAs (DECs) in serum exosomal samples and GSE79634 tissue samples was conducted. A specific CircRNA database was applied to investigate DECs binding miRNAs. Target genes were predicted using the R package multiMiR. Cox regression analyses were applied for constructing a prognostic model. The immunological characteristics analysis was carried out through the TIMER, QUANTISEQ, XCELL, EPIC, and ssGSEA algorithms.Results15 DECs were finally identified, and a circRNA-miRNA-mRNA network was established. A prognostic risk model was developed to categorize patients according to the risk scores. Furthermore, the association between risk score and immune checkpoint genes including CD80, TNFSF9, CD276, CD274, LGALS9, and CD44 were significantly elevated in the high-risk group, while ICOSLG and ADORA2A were upregulated in the low-risk group.ConclusionsOur results may provide new clues for the prognosis and treatment of PDAC.     

An exosome-related lncRNA signature correlates with prognosis,immune microenvironment,and therapeutic responses in hepatocellular carcinoma

BackgroundExosomes act as essential modulators of cancer development and progression in hepatocellular carcinoma. However, little is known about the potential prognostic value and underlying molecular features of exosome-related long non-coding RNAs.MethodsGenes associated with exosome biogenesis, exosome secretion, and exosome biomarkers were collected. Exosome-related lncRNA modules were identified using PCA and WGCNA analysis. A prognostic model based on data from the TCGA, GEO, NODE, and ArrayExpress was developed and validated. A comprehensive analysis of the genomic landscape, functional annotation, immune profile, and therapeutic responses underlying the prognostic signature was performed on multi-omics data, and bioinformatics methods were also applied to predict potential drugs for patients with high risk scores. qRT-PCR was used to validate the differentially expressed lncRNAs in normal and cancer cell lines.ResultsTwenty-six hub lncRNAs were identified as highly correlated with exosomes and overall survival and were used for prognosis modeling. Three cohorts consistently showed higher scores in the high-risk group, with an AUC greater than 0.7 over time. These higher scores implied poorer overall survival, higher genomic instability, higher tumor purity, higher tumor stemness, pro-tumor pathway activation, lower anti-tumor immune cell and tertiary lymphoid structure infiltration, and poor responses to immune checkpoint blockade therapy and transarterial chemoembolization therapy.ConclusionThrough developing an exosome-related lncRNA predictor for HCC patients, we revealed the clinical relevance of exosome-related lncRNAs and their potential as prognostic biomarkers and therapeutic response predictors.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

Hypoxia-related LncRNAs signature predicts prognosis and is associated with immune infiltration and progress of head and neck squamous cell carcinoma

BackgroundDisclosing prognostic information is necessary to enable good treatment selection and improve patient outcomes. Previous studies suggest that hypoxia is associated with an adverse prognosis in patients with HNSCC and that long non-coding RNAs (lncRNAs) show functions in hypoxia-associated cancer biology. Nevertheless, the understanding of lncRNAs in hypoxia related HNSCC progression remains confusing.MethodsData were downloaded from TCGA and GEO database. Bioinformatic tools including R packages GEOquery, limma, pheatmap, ggplot2, clusterProfiler, survivalROC and survcomp and LASSO cox analysis were utilized. Si-RNA transfection, CCK8 and real-time quantified PCR were used in functional study.ResultsGEO data (GSE182734) revealed that lncRNA regulation may be important in hypoxia related response of HNSCC cell lines. Further analysis in TCGA data identified 314 HRLs via coexpression analysis between differentially expressed lncRNAs and hypoxia-related mRNAs. 23 HRLs were selected to build the prognosis predicting model using lasso Cox regression analyses. Our model showed excellent performance in predicting survival outcomes among patients with HNSCC in both the training and validation sets. We also found that the risk scores were related to tumor stage and to tumor immune infiltration. Moreover, LINC01116 were selected as a functional study target. The knockdown of LINC01116 significantly inhibited the proliferation of HNSCC cells and effected the hypoxia induced immune and the NF-κB/AKT signaling.ConclusionsData analysis of large cohorts and functional experimental validation in our study suggest that hypoxia related lncRNAs play an important role in the progression of HNSCC, and its expression model can be used for prognostic prediction.     

PRF1 is a prognostic marker and correlated with immune infiltration in head and neck squamous cell carcinoma

PurposeHead and neck squamous cell carcinoma (HNSCC) is a highly invasive malignancy with poor survival. Perforin (PRF1) plays essential roles in host immunity. Our research intended to identify the correlations of PRF1 with clinical prognosis and tumor immune infiltration in HNSCC.MethodsWe explored PRF1 expression and its associations with the clinical features of HNSCC via the Tumor Immune Estimation Resource (TIMER), Oncomine and The Cancer Genome Atlas (TCGA) databases. The prognostic value of PRF1 for HNSCC was further explored by Kaplan–Meier plotter and TIMER. Finally, the relation between PRF1 and immune infiltration in HNSCC was estimated via CIBERSORT and TIMER.ResultsPRF1 expression was remarkably elevated in HNSCC and associated with clinical stage and HPV infection. High PRF1 expression predicted favorable outcomes in HNSCC, especially in HPV+ HNSCC. Moreover, higher infiltration of CD8+ T cells and CD4+ T cells were found in the PRF1^high group of HNSCC. PRF1 expression in HNSCC was strongly correlated with infiltrating CD8+ T cells and dendritic cells (DCs), with higher relevance in HPV+ HNSCC.ConclusionOur findings suggested that PRF1 could be a novel prognostic biomarker in HNSCC and that its expression was related to immune cell infiltration, which was impacted by HPV status.Key words: PRF1, prognosis, head and neck squamous cell carcinoma, tumor immune infiltration, HPV     

LAMB3在头颈鳞癌中的表达及预后价值分析

摘要 目的：研究层黏连蛋白亚基β3（LAMB3）在头颈鳞癌（HNSCC）中的表达、预后价值、免疫相关性及作用机制。方法：利用基于癌症基因组图谱（TCGA）的多个在线数据库,分析LAMB3在HNSCC肿瘤组织中的差异表达、预后价值以及对免疫细胞浸润的影响;通过基因共表达及蛋白-蛋白相互作用网络分析,探究LAMB3参与HNSCC的生物过程以及相关信号通路;预测调控LAMB3基因的上游靶miRNA。结果：LAMB3在HNSCC中高表达,LAMB3表达水平与年龄分布、肿瘤病理分级、淋巴结转移状态和HPV病毒感染状态相关;预后分析显示LAMB3在HNSCC中高表达预示患者不良预后,在肿瘤分期为Stage3、白色人种、肿瘤病理分期为Grade1和Grade2以及肿瘤新生抗原高表达HNSCC患者中总生存期更短;LAMB3表达水平影响HNSCC肿瘤微环境,LAMB3高表达能够降低B细胞、CD8⁺T 细胞浸润程度,升高CD4⁺T细胞浸润程度;基因共表达和功能相关分析表明,LAMB3高表达与基底膜形成、细胞连接、细胞迁移等生物过程有关;LAMB3潜在靶miRNA是hsa-miR-24-3p。结论：LAMB3在 HNSCC中高表达与免疫细胞浸润和患者不良预后相关,可作为HNSCC发病风险和预后指标。     

Construction of a paclitaxel-related competitive endogenous RNA network and identification of a potential regulatory axis in pancreatic cancer

BackgroundIncreasing numbers of studies have elucidated the role of competitive endogenous RNA (ceRNA) networks in carcinogenesis. However, the potential role of the paclitaxel-related ceRNA network in the innate mechanism and prognosis of pancreatic cancer has not been identified.MethodsComprehensive bioinformatics analyses were performed to identify drug-related miRNAs (DRmiRNAs), drug-related mRNAs (DRmRNAs) and drug-related lncRNAs (DRlncRNAs) and construct a ceRNA network. The ssGSEA and CIBERSORT algorithms were utilized for immune cell infiltration analysis. Additionally, we validated our paclitaxel-related ceRNA regulatory axis at the gene expression level; functional experiments were conducted to explore the biological functions of the key genes.ResultsA total of 182 mRNAs, 13 miRNAs, and 53 lncRNAs were confirmed in the paclitaxel-related ceRNA network. In total, 6 mRNAs, 4 miRNAs, and 6 lncRNAs were identified to establish a risk signature and exhibited optimal prognostic effects. The mRNA signature can predict the abundance of immune cell infiltration and the sensitivity of different chemotherapeutic drugs and may also have a guiding effect in immune checkpoint therapy. A potential PART1/hsa-mir-21/SCRN1 axis was confirmed according to the ceRNA theory and was verified by qPCR. The results indicated that PART1 knockdown markedly increased hsa-mir-21 expression but inhibited SCRN1 expression, weakening the proliferation and migration abilities.ConclusionsWe hypothesized that the paclitaxel-related ceRNA network strongly influences the innate mechanism, prognosis, and immune infiltration of pancreatic cancer. Our risk signatures can accurately predict survival outcomes and provide a clinical basis.     

Five-Year Prognosis Model of Esophageal Cancer Based on Genetic Algorithm Improved Deep Neural Network

ObjectivesEsophageal cancer is a high occult malignant tumor. Even with good diagnosis and treatment, the 5-year survival rate of esophageal cancer patients is still less than 30%. Considering the influence of clinical characteristics on postoperative esophageal cancer patients, the construction of a neural network model will help improve the poor prognosis of patients in the five years.Material and methodsIn this study, genetic algorithm optimized deep neural network is exploited to the clinical dataset of esophageal cancer. The independent prognostic factors are screened by Relief algorithm and Cox proportional risk regression. FTD prognostic staging system is established to assess the risk level of esophageal cancer patients.ResultsFTD staging system and independent prognostic factors are integrated into the genetic algorithm optimized deep neural network. The Area Under Curve (AUC) of FTD staging system is 0.802. FTD staging system is verified by the Kaplan-Meier survival curve, and the median survival time is divided for different risk grades. The FTD staging system is superior to the TNM stages in the prognosis effect. The AUC of deep neural network optimized by genetic algorithm is 0.91.ConclusionThe deep neural network optimized by genetic algorithm has good performance in predicting the 5-year survival status of esophageal cancer patients. The FTD staging system has a significant prognostic effect. The FTD staging system and genetic algorithm optimized deep neural network can be successfully availed in clinical diagnosis and treatment.