Genome-wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma with varied prognosis. We aimed to identify and assess the possible prognostic long noncoding RNA (lncRNA) biomarkers. LncRNAs expression data and corresponding clinical information of 619 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes analysis, univariate Cox regression, the least absolute shrinkage and selection operator Cox regression model were utilized to identify hub lncRNAs. Multivariate Cox regression was used to establish the risk model. Statistical analysis was performed using R 3.5.3. The expression value of five lncRNAs and the risk-score levels were significantly associated with a survival prognosis of ccRCC patients (all P < .001). In the TCGA validation cohort, the area under the curve (AUC) for the integrated nomogram was 0.905 and 0.91 for 3-, 5-year prediction separately. The AUC reached up to 0.757 in an independent ICGC cohort. Besides, the calibration plots also illustrated well curve-fitting between observation values and predictive values. Weighted gene co-expression network analysis and subsequent pathway analysis revealed that the PI3K-Akt-mTOR and hypoxia-inducible factor signaling crosstalk might function as the most essential mechanisms related to the five-lncRNAs signature. Our study suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234, and LINC01885 were significantly associated with ccRCC prognosis. The prognostic model based on this five lncRNA may predict the overall survival of ccRCC.     

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.     

肾透明细胞癌预后相关miRNAs的生物信息学分析

目的寻找可作为肾透明细胞癌（ccRCC）生物标志物的miRNA,以及ccRCC与正常组织间miRNA差异表达情况。 方法利用TCGA数据库下载ccRCC中miRNA表达数据,分析肿瘤与正常组织间差异表达miRNA。使用Kaplan-Meier曲线对患者进行生存分析,筛选出表达情况与临床预后相关的miRNA。通过生物信息学对miRNA的靶基因进行预测,然后运用FunRich软件和ClueGO对靶基因进行GO和KEGG富集分析。 结果通过TCGA数据库分析发现,ccRCC较正常组织差异表达miRNA共54个,其中上调33个,下调21个。通过生存分析发现hsa-miR-21和hsa-miR-155与患者预后相关,P≤0.05。进一步通过Perl软件在Targetscan、miRDB、miRTarBase、miRPath这四个数据库中预测miRNA靶基因并将结果取交集,共发现129个靶基因。GO和KEGG分析结果表明,这些靶基因主要与转录因子活性、信号转导以及FoxO、TNF等信号通路密切相关。 结论通过生物信息学分析发现了ccRCC与正常组织的差异表达miRNA;其中hsa-miR-21和hsa-miR-155与患者总体生存率相关,并通过调控靶基因参与相关的信号通路进而影响ccRCC的发生发展进程,提示hsa-miR-21和hsa-miR-155可能是ccRCC潜在的生物标志物。     

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.     

Identification of immune-related hub genes and construction of an immune-related gene prognostic index for low-grade glioma

Low-grade glioma (LGG) poses significant management challenges and has a dismal prognosis. While immunotherapy has shown significant promise in cancer treatment, its progress in glioma has confronted with challenges. In our study, we aimed to develop an immune-related gene prognostic index (IRGPI) which could be used to evaluate the response and efficacy of LGG patients with immunotherapy. We included a total of 529 LGG samples from TCGA database and 1152 normal brain tissue samples from the GTEx database. Immune-related differentially expressed genes (DEGs) were screened. Then, we used weighted gene co-expression network analysis (WGCNA) to identify immune-related hub genes in LGG patients and performed Cox regression analysis to construct an IRGPI. The median IRGPI was used as the cut-off value to categorize LGG patients into IRGPI-high and low subgroups, and the molecular and immune mechanism in IRGPI-defined subgroups were analysed. Finally, we explored the relationship between IRGPI-defined subgroups and immunotherapy related indicators in patients after immunotherapy. Three genes (RHOA, NFKBIA and CCL3) were selected to construct the IRGPI. In a survival analysis using TCGA cohort as a training set, patients in the IRGPI-low subgroup had a better OS than those in IRGPI-high subgroup, consistent with the results in CGGA cohort. The comprehensive results showed that IRGPI-low subgroup had a more abundant activated immune cell population and lower TIDE score, higher MSI, higher TMB score, lower T cell dysfunction score, more likely benefit from ICIs therapy. IRGPI is a promising biomarker in the field of LGG ICIs therapy to distinguish the prognosis, the molecular and immunological characteristics of patients.     

DDX39 as a predictor of clinical prognosis and immune checkpoint therapy efficacy in patients with clear cell renal cell carcinoma

DEAD-box protein 39 (DDX39) has been demonstrated to be a tumorigenic gene in multiple tumor types, but its role in the progression and immune microenvironment of clear cell renal cell cancer (ccRCC) remains unclear. The aim of the present study was to investigate the role of DDX39 in the ccRCC tumor progression, immune microenvironment and efficacy of immune checkpoint therapy.The DDX39 expression level was first detected in tumors in the public data and then verified in ccRCC samples from Changzheng Hospital. The prognostic value of DDX39 expression was assessed in the Cancer Genome Atlas (TCGA) and ccRCC patients from Changhai Hospital. The role of DDX39 in promoting ccRCC was analyzed by bioinformatic analysis and in vitro experiments. The association between DDX39 expression and immune cell infiltration and immune inhibitory markers was analyzed, and its value in predicting the immune checkpoint therapy efficacy in ccRCC were evaluated in the public database. DDX39 expression was elevated in Oncomine, GEO and TCGA ccRCC databases, as well as in Changzheng ccRCC samples. In TCGA ccRCC patients, increased DDX39 expression predicted worse overall survival (OS) (p<0.0001) and progression-free interval (PFI) (p<0.0001), and was shown as an independent predictive factor for OS (p=0.002). These findings were consistent with those from Changhai ccRCC patients. In addition, GO and GSEA analysis identified DDX39 as a pro-ccRCC gene. In vitro experiments confirmed the role of DDX39 in promoting ccRCC cell. Finally, DDX39 was found to be positively correlated with a variety of immune inhibitory markers, and could predict the adverse efficacy of immune checkpoint therapy in TIDE analysis. In conclusion, Increased DDX39 in ccRCC patients predicted worse clinical prognosis, promoted ccRCC cell proliferation, migration and invasion, and also predicted adverse efficacy of immune checkpoint therapy.     

Development and validation of a CTNNB1-associated metabolic prognostic model for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy closely related to metabolic reprogramming. We investigated how CTNNB1 mutation regulates the HCC metabolic phenotype and thus affects the prognosis of HCC. We obtained the mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA), the International Cancer Genomics Consortium (ICGC) and the Gene Expression Omnibus database ( GSE14520 and GSE116174 ). We conducted gene set enrichment analysis on HCC patients with and without mutant CTNNB1 through TCGA dataset. The Kaplan-Meier analysis and univariate Cox regression analysis assisted in screening metabolic genes related to prognosis, and the prognosis model was constructed using the Lasso and multivariate Cox regression analysis. The prognostic model showed good prediction performance in both the training cohort (TCGA) and the validation cohorts (ICGC, GSE14520 , GSE116174 ), and the high-risk group presented obviously poorer overall survival compared with low-risk group. Cox regression analysis indicated that the risk score can be used as an independent predictor for the overall survival of HCC. The immune infiltration in different risk groups was also evaluated in this study to explore underlying mechanisms. This study is also the first to describe an metabolic prognostic model associated with CTNNB1 mutations and could be implemented for determining the prognoses of individual patients in clinical practice.     

FBXL6 depletion restrains clear cell renal cell carcinoma progression

BackgroundF-box proteins play important roles in cell cycle and tumorigenesis. However, its prognostic value and molecular function in clear cell renal cell carcinoma (ccRCC) remain unclear. In this study, we established a survival model to evaluate the prognosis of patients with ccRCC using the F-box gene signature and investigated the function of FBXL6 in ccRCC.MethodsComprehensive bioinformatics analyses were used to identify differentially expressed F-box and hub genes associated with ccRCC carcinogenesis. Based on the F-box gene signature, we constructed a risk model and nomogram to predict the overall survival (OS) of patients with ccRCC and assist clinicians in decision-making. Finally, we verified the function and underlying molecular mechanisms of FBXL6 in ccRCC using CCK-8 and EdU assays, flow cytometry, and subcutaneous xenografts.ResultsA risk model based on FBXO39, FBXL6, FBXO1, and FBXL16 was developed. In addition, we drew a nomogram based on the risk score and clinical features to assess the prognosis of patients with ccRCC. Subsequently, we identified FBXL6 as an independent prognostic marker that was highly expressed in ccRCC cell lines. In vivo and in vitro assays revealed that the depletion of FBXL6 inhibited cell proliferation and induced apoptosis. We also demonstrated that SP1 regulated the expression of FBXL6.ConclusionsFBXL6 was first identified as a diagnostic and prognostic marker in patients with ccRCC. Loss of FBXL6 attenuates proliferation and induces apoptosis in ccRCC cells. SP1 was also found to regulate the expression of FBXL6.     

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.     

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。     

Identification of a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival-related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein-protein interaction analysis was conducted for the prognostic AS-related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival-specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival-specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <−0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.     

A panel of eight autophagy-related long non-coding RNAs is a good predictive parameter for clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) carries a variable prognosis. Prognostic biomarkers can stratify patients according to risk, and can provide crucial information for clinical decision-making. We screened for an autophagy-related long non-coding lncRNA (lncRNA) signature to improve postoperative risk stratification in The Cancer Genome Atlas (TCGA) database. We confirmed this model in ICGC and SYSU cohorts as a significant and independent prognostic signature. Western blotting, autophagic-flux assay and transmission electron microscopy were used to verify that regulation of expression of 8 lncRNAs related to autophagy affected changes in autophagic flow in vitro. Our data suggest that 8-lncRNA signature related to autophagy is a promising prognostic tool in predicting the survival of patients with ccRCC. Combination of this signature with clinical and pathologic parameters could aid accurate risk assessment to guide clinical management, and this 8-lncRNAs signature related to autophagy may serve as a therapeutic target.     

Novel prognostic model established for patients with head and neck squamous cell carcinoma based on pyroptosis-related genes

We aimed at establishing a risk – score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). A total of 33 pyroptosis-related genes were selected. We then evaluated the data of 502 HNSCC patients and 44 normal patients from TCGA database. Gene expression was then profiled to detect differentially expressed genes (DEGs). Using the univariate, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we generated a risk – score model. Tissue samples from neoplastic and normal sites of 44 HNSCC patients were collected. qRT-PCR were employed to analyze the mRNA level of the samples. Kaplan-Meier method was used to evaluate the overall survival rate (OS). Enrichment analysis was performed to elucidate the underlying mechanism of HNSCC patient's differentially survival status from the perspective of tumor immunology. 17 genes were categorized as DEGs. GSDME, IL-6, CASP8, CASP6, NLRP1 and NLRP6 were used to establish the risk – score model. Each patient's risk score in the TCGA cohort was calculated using the risk – score formula. The risk score was able to independently predict the OS of the HNSCC patients (P = 0.02). The OS analysis showed that the risk score model (P < 0.0001) was more reliable than single gene, a phenomenon verified by practical patient cohort. Additionally, enrichment analysis indicated more active immune activities in low-risk group than high-risk group. In conclusion, our risk – score model has provided novel strategy for the prediction of HNSCC patients’ prognosis.     

Novel prognostic model established for patients with head and neck squamous cell carcinoma based on pyroptosis-related genes

We aimed at establishing a risk – score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). A total of 33 pyroptosis-related genes were selected. We then evaluated the data of 502 HNSCC patients and 44 normal patients from TCGA database. Gene expression was then profiled to detect differentially expressed genes (DEGs). Using the univariate, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we generated a risk – score model. Tissue samples from neoplastic and normal sites of 44 HNSCC patients were collected. qRT-PCR were employed to analyze the mRNA level of the samples. Kaplan-Meier method was used to evaluate the overall survival rate (OS). Enrichment analysis was performed to elucidate the underlying mechanism of HNSCC patient's differentially survival status from the perspective of tumor immunology. 17 genes were categorized as DEGs. GSDME, IL-6, CASP8, CASP6, NLRP1 and NLRP6 were used to establish the risk – score model. Each patient's risk score in the TCGA cohort was calculated using the risk – score formula. The risk score was able to independently predict the OS of the HNSCC patients (P = 0.02). The OS analysis showed that the risk score model (P < 0.0001) was more reliable than single gene, a phenomenon verified by practical patient cohort. Additionally, enrichment analysis indicated more active immune activities in low-risk group than high-risk group. In conclusion, our risk – score model has provided novel strategy for the prediction of HNSCC patients’ prognosis.     

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.     

A robust six-miRNA prognostic signature for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a major health problem worldwide. We aimed to identify a robust microRNA (miRNA)-based signature for predicting HNSCC prognosis. The miRNA expression profiles of HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA HNSCC cohort was randomly divided into the discovery and validation cohort. A miRNA-based prognostic signature was built up based on TGCA discovery cohort, and then further validated. The downstream targets of prognostic miRNAs were subjected to functional enrichment analyses. The role of miR-1229-3p, a prognosis-related miRNA, in tumorigenesis of HNSCC was further evaluated. A total of 305 significantly differentially expressed miRNAs were found between HNSCC samples and normal tissues. A six-miRNA prognostic signature was constructed, which exhibited a strong association with overall survival (OS) in the TCGA discovery cohort. In addition, these findings were successfully confirmed in TCGA validation cohort and our own independent cohort. The miRNA-based signature was demonstrated as an independent prognostic indicator for HNSCC. A risk signature-based nomogram model was constructed and showed good performance for predicting the OS for HNSCC. The functional analyses revealed that the downstream targets of these prognostic miRNAs were closely linked to cancer progression. Mechanistically, in vitro analysis revealed that miR-1229-3p played a tumor promoting role in HNSCC. In conclusion, our study has developed a robust miRNA-based signature for predicting the prognosis of HNSCC with high accuracy, which will contribute to improve the therapeutic outcome.