INHA acts as a novel and potential biomarker in lung adenocarcinoma and shapes the immune-suppressive tumor microenvironment

BackgroundINHA expression has been correlated with the development, growth, and progression of multiple cancer types. However, the biological role of INHA has not been investigated in patients with lung adenocarcinoma (LUAD). Here, we performed a comprehensive bioinformatics analysis of the LUAD dataset to determine the mechanisms underlying the regulation of tumorigenesis by INHA.Materials and methodsINHA expression and clinical information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Protein levels in LUAD cell lines and human lung epithelial cells were examined by western blotting. Next, the prognostic value of INHA in LUAD was assessed using Cox regression analysis, while the potential biological functions and the impact on the immune microenvironment of INHA were investigated using gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA). Finally, the effect of INHA on LUAD cell proliferation and invasion was determined in vitro and in vivo.ResultsWe found significantly high mRNA and protein expression levels of INHA in LUAD tissues and cell lines. Additionally, a higher expression of INHA was linked to a shorter overall survival (OS) and a worse pathological stage, while INHA expression was associated with immune cell infiltration and immune-related markers in the LUAD tumor microenvironment. LUAD with high INHA expression tends to be a cold tumor. Furthermore, GO and KEGG enrichment analysis indicated that INHA-related genes were enriched in the cell adhesion and immune signaling pathways of LUAD. INHA promoted LUAD cell proliferation and invasion, in vitro and in vivo, by inducing the EGFR pathway.ConclusionOur findings revealed that INHA is overexpressed in LUAD and is linked to a poor prognosis. Our study demonstrates the potential of INHA as an immunotherapeutic and predictive biomarker in LUAD.     

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.     

Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression–based retrospective study

Lung cancer is one of the fatal tumors. The tumor microenvironment plays a key role in regulating tumor progression. To figure out the role of tumor microenvironment in lung adenocarcinoma (LUAD), ESTIMATE algorithm was used to evaluate the immune scores in LUAD. Patients with low immune scores had a worse overall survival (OS) compared with high immune scores. Using RNA-Seq data of 489 patients in The Cancer Genome Atlas (TCGA), differentially expressed genes (DEGs) were identified between high- and low-immune score groups. Based on the DEGs, nine-gene signature was constructed by the least absolute shrinkage and selection operator Cox regression model in TCGA set. The signature demonstrated significant prognostic value in both TCGA and Gene Expression Omnibus database. Multivariate Cox regression analyses indicated that nine-genes signature was an independent prognostic factor. Subgroup analysis also revealed a robust prognostic ability of nine-gene signature. A nomogram with a C-index of 0.722 had a favorable power for predicting 3-, 5-, and 10-year survival for clinical use by integrating nine-gene signature and other clinical features. Co-expression and functional enrichment analysis showed that nine-gene signature was significantly associated with immune response and provided potential profound molecules for revealing the mechanism of tumor initiation and progression. In conclusion, we revealed the significance of immune infiltration and built a novel nine-gene signature as a reliable prognostic factor for patients with LUAD.     

Development of a tumor microenvironment-related prognostic signature in glioma to predict immune landscape and potential therapeutic drugs

The involvement of the tumor microenvironment (TME) in the biology of gliomas has expanded, while it is yet uncertain its potential of supporting diagnosis and therapy choices. According to immunological characteristics and overall survival, cohorts of glioma patients from public databases were separated into two TME-relevant clusters in this analysis. Based on differentially expressed genes between TME clusters and correlative regression analysis, a 21-gene molecular classifier of TME-related prognostic signature (TPS) was constructed. Afterward, the prognostic efficacy and effectiveness of TPS were assessed in the training and validation groups. The outcome demonstrated that TPS might be utilized alone or in conjunction with other clinical criteria to act as a superior prognostic predictor for glioma. Also, high-risk glioma patients classified by TPS were considered to associate with enhanced immune infiltration, greater tumor mutation, and worse general prognosis. Finally, possible treatment medicines specialized for different risk subgroups of TPS were evaluated in drug databases.     

A tumor-associated macrophages related model for predicting biochemical recurrence and tumor immune environment in prostate cancer

ObjectiveTo identify tumor-associated macrophages (TAMs) related molecular subtypes and develop a TAMs related prognostic model for prostate cancer (PCa).MethodsConsensus clustering analysis was used to identify TAMs related molecular clusters. A TAMs related prognostic model was developed using univariate and multivariate Cox analysis.ResultsThree TAMs related molecular clusters were identified and were confirmed to be associated with prognosis, clinicopathological characteristics, PD-L1 expression levels and tumor microenvironment. A TAMs related prognostic model was constructed. Patients in low-risk group all showed a more appreciable biochemical recurrence-free survival (BCRFS) than patients in high-risk group in train cohort, test cohort, entire TCGA cohort and validation cohort. SLC26A3 attenuated progression of PCa and prevented macrophage polarizing to TAMs phenotype, which was initially verified.ConclusionsWe successfully identified molecular clusters related to TAMs. Additionally, we developed a prognostic model involving TAMs that exhibits excellent predictive performance for biochemical recurrence-free survival in PCa.     

Pyroptosis in glioblastoma: A crucial regulator of the tumour immune microenvironment and a predictor of prognosis

Recent studies have shown that pyroptosis, an inflammatory form of cell death, has a dual role in tumorigenesis and tumour progression and affects the prognosis of patients; however, the role of pyroptosis in glioblastoma (GBM) is still unclear. In this study, based on GBM patients'' data from two independent cohorts, we performed a comprehensive analysis of the expression and prognostic value of 33 pyroptosis‐associated genes (PAGs) in GBM, as well as their role in the tumour immune microenvironment (TIME) of GBM. We identified 29 PAGs that were differentially expressed between GBM and normal brain tissue, 18 of which were upregulated in GBM tissue. Most of the 33 PAGs were strongly correlated with the levels of immune cell infiltration. Based on the 33 PAGs, the GBM samples can be divided into two clusters (C1‐C2), with C1 having a ‘hot’ but immunosuppressive TIME and C2 having a ‘cold’ TIME, suggesting different immunotherapeutic responses in the two clusters. In addition, we identified four PAGs that were strongly associated with GBM prognosis and constructed a risk model based on these four PAGs. This risk model is an independent prognostic factor for GBM patients, and there is a different immune status between high‐ and low‐risk groups. In conclusion, this study demonstrates that pyroptosis is closely associated with the prognosis and TIME of GBM and provides an important basis for further studies on the relationship between pyroptosis and GBM.     

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.     

The joint role of methylation and immune-related lncRNAs in ovarian cancer: Defining molecular subtypes and developing prognostic signature

IntroductionComplex outcome of ovarian cancer (OC) stems from the tumor immune microenvironment (TIME) influenced by genetic and epigenetic factors. This study aimed to comprehensively explored the subclasses of OC through lncRNAs related to both N6-methyladenosine (m6A)/N1-methyladenosine (m1A)/N7-methylguanosine (m7G)/5-methylcytosine (m5C) in terms of epigenetic variability and immune molecules and develop a new set of risk predictive systems.Material and methodsThe lncRNA data of OC were collected from TCGA. Spearman correlation analysis on lncRNA data of OC with immune-related gene expression and with m6A/m5C/m1A/m7G were respectively conducted. The m6A/m5C/m1A/m7G-related m6A/m5C/m1A/m7G related immune lncRNA subtypes were identified on the basis of the prognostic lncRNAs. Heterogeneity among subtypes was evaluated by tumor mutation analysis, tumor microenvironment (TME) component analysis, response to immune checkpoint blocked (ICB) and chemotherapeutic drugs. A risk predictive system was developed based on the results of Cox regression analysis and random survival forest analysis of the differences between each specific cluster and other clusters.ResultsThree m6A/m5C/m1A/m7G-related immune lncRNA subtypes of OC showing distinct differences in prognosis, mutation pattern, TIME components, immunotherapy and chemotherapy response were identified. A set of risk predictive system consisting of 10 lncRNA for OC was developed, according to which the risk score of samples in each OC dataset was calculated and risk type was defined.ConclusionsThis study classified three m6A/m5C/m1A/m7G-related immune lncRNA subtypes with distinct heterogeneous mutation patterns, TME components, ICB therapy and immune response, and provided a set of risk predictive system consisted of 10 lncRNA for OC.     

Identification of immunogenic cell death-associated subtypes and characterization of the tumor microenvironment in endometrial cancer

Immunogenic cell death (ICD) is one of the mechanisms regulating cell death, which activates adaptive immunity in immunocompetent hosts and is associated with tumor progression, prognosis and therapeutic response. Endometrial cancer (EC) is one of the most common malignancies of the female genital tract, and the potential role of immunogenic cell death-related genes (IRGs) in the tumor microenvironment (TME) remains unclear. We describe the variation of IRGs and assess the expression patterns in EC samples from The Cancer Genome Atlas and Gene Expression Omnibus cohorts. Based on the expression of 34 IRGs, we identified two different ICD-related clusters and subsequently differentially expressed genes between the two ICD-related clusters were used for the identification of two ICD gene clusters. We identified the clusters and found that alterations in the multilayer IRG were associated with patient prognosis and TME cell infiltration characteristics. On this basis, ICD score risk scores were calculated, and ICD signatures were constructed and validated for their predictive power in EC patients. To help clinicians better apply the ICD signature, an accurate nomogram was constructed. The low ICD risk group was characterized by high microsatellite instability, high tumor mutational load, high IPS score and stronger immune activation. Our comprehensive analysis of IRGs in EC patients suggested a potential role in the tumor immune interstitial microenvironment, clinicopathological features and prognosis. These findings may improve our understanding of the role of ICDs, and provide a new basis for assessing prognosis and developing more effective immunotherapeutic strategies in EC.     

基于在线数据库和真实世界样本探讨肺腺癌患者POLE/POLD1突变的临床意义

摘要 目的：POLE和POLD1突变导致DNA聚合酶校对功能丧失可能会影响基因组稳定性并导致突变增加和肿瘤形成。本文结合在线数据库和真实世界样本进一步分析肺腺癌（LUAD）患者POLE和/或 POLD1 突变的临床意义。方法：纳入2021年1月～2021年8月徐州医科大学附属医院肺癌术后组织标本115例,利用二代测序技术（NGS）检测基因突变;从癌症基因组图谱（TCGA）数据库收集肺腺癌数据集,通过Cbioportal在线数据库获得肿瘤突变分布图,通过Cibersort法计算获得样本的免疫相关细胞浸润情况。结果：真实世界样本中POLE/ POLD1突变的比例为7.83%（9/115）。TCGA数据显示POLE/POLD1突变的LUAD患者总生存期（OS）减少（P=0.0359）。然而,携带该突变的患者并发其他基因改变的频率明显增加,尤其是与TP53突变存在正相关;同时,POLE/POLD1突变与LUAD组织浸润性免疫杀伤细胞呈正相关,与免疫抑制细胞呈负相关,提示这部分患者对免疫检查点抑制剂（ICI）敏感。结论：LUAD患者POLE/POLD1突变预示较高的肿瘤突变负荷和免疫微环境改变,可作为ICI疗效预测的潜在生物标志物,值得临床关注。     

丝氨酸蛋白酶抑制剂(SERPIN)家族相关基因在结肠腺癌中预后模型的建立及应用

应用生物信息学方法,构建结肠腺癌(COAD)丝氨酸蛋白酶抑制剂(SERPIN)家族相关基因预后模型。从TCGA数据库和GEO数据库下载结肠腺癌(COAD)转录组和临床数据,根据数据中SERPINs家族基因的表达量对COAD患者进行一致性聚类分析;将数据随机均分为训练集(Train)组和验证集(Test)组,基于两个亚型的差异基因,利用Train组进行COX回归和Lasso回归构建预后模型,根据模型风险评分中位值将样本分为高、低风险两组,绘制高低风险组患者生存曲线;通过ROC曲线评价模型预测能力;利用Test组数据验证模型;构建列线图,评估患者生存率模型预测值与实际值的一致性;并利用利用ESTIMATE算法和CIBERSORT算法评估风险评分和肿瘤微环境(TME)以及免疫浸润的相关性。通过34个SERPIN基因确定了两个亚型,基于2个亚型筛选出了436个预后相关分型差异基因,通过Lasso回归确定出了11个预后相关基因参与风险模型的构建,根据模型评分区分的高低风险组具有明显的生存差异,列线图可以准确预测1、3和5年生存率。肿瘤微环境分析和免疫浸润分析显示高风险评分组患者免疫活性差。SERPIN家族相关基因构建的风险评分模型能够预测COAD的预后,有利于进一步指导临床对COAD的诊治,提高患者生存率。     

SIRT7琥珀酰化修饰对肝癌生存、免疫浸润及预后的相关性分析

摘要 目的：探究SIRT7基因琥珀酰化修饰对肝癌患者的生存、免疫浸润及预后的相关性分析。方法：采用生物信息分析法对SIRT7在肝癌组织中的表达情况及其对肝癌患者预后的影响进行分析；采用蛋白免疫印迹法（Western blot）检测其转染效果。结果：（1）生物信息分析结果显示：SIRT7在多种肿瘤（包括肝癌）组织中呈高表达（P<0.05）；SIRT7的表达与肿瘤的生存曲线相关（P<0.05）；肝癌患者的SIRT7相对表达量与其预后相关，高表达组肝癌患者的总生存情况（P=0.017）和无进展生存情况较低表达组缩短（P=0.004）；免疫浸润和肿瘤微环境分析结果显示，SIRT7表达水平与多数免疫细胞浸润水平、肿瘤微环境(ESTIMATES core)均有明显负相关。（2）Western blot显示，SIRT7在肝癌细胞中表达高于正常细胞。因此，SIRT7 可作为肝癌的潜在预后标志物。结论：SIRT7表达水平与肝癌(HCC)患者的预后、免疫细胞浸润性、肿瘤微环境免疫细胞和基质细胞浸润等相关。     

Prognostic value of metabolic genes in lung adenocarcinoma via integrative analyses

BackgroundLung adenocarcinoma (LUAD) is the most common malignant lung tumor. Metabolic pathway reprogramming is an important hallmark of physiologic changes in cancers. However, the mechanisms through which these metabolic genes and pathways function in LUAD as well as their prognostic values have not been fully established.MethodsFour publicly available datasets from GEO and TCGA were used to identify differentially expressed genes (DEGs) in LUAD, which were then subjected to GO and KEGG pathway enrichment analysis. Associations between metabolic gene expressions with overall survival, tumor stage, TP53 mutation status, and infiltrated immune cells were investigated. Protein-protein interactions were evaluated using GeneMANIA and Metascape.ResultsBy integrating four public datasets, 247 DEGs were identified in LUAD. These DEGs were significantly enriched in regulation of chromosome segregation, centromeric region, and histone kinase activity GO terms, as well as in cell cycle, p53 signaling pathway, metabolic pathways, and other KEGG pathways. Elevated expressions of ten metabolic genes in LUAD were significantly associated with poor survival outcomes. These metabolic genes were highly expressed in more advanced tumor stage and TP53 mutated patients. Moreover, expression levels were significantly correlated with tumor-infiltrating immune cells. PPI interaction analysis revealed that the top 20 genes interacting with each metabolic gene were significantly enriched in DNA replication, response to radiation, and central carbon metabolism in cancer.ConclusionThis study elucidates on molecular changes in metabolic genes in LUAD, which may inform the development of genetically oriented diagnostic approaches and effective treatment options.     

A signature of immune-related gene pairs predicts oncologic outcomes and response to immunotherapy in lung adenocarcinoma

In this study, we established the predictive model for lung adenocarcinoma (LUAD) depending on immune-related gene pairs (IRGPs) signature, which could not consider the technical bias of different platforms. Furthermore, we explored the predictive model with regard to the immune microenvironment and response to immunotherapy and identified specific drugs targeting the IRGPs model. Twenty-three IRGPs were identified and comprised the predictive model. When compared with the high-risk group, the low-risk group displayed a distinctly favorable prognosis and was characterized by increased immune score and decreased tumor purity. In addition, the low-risk group exhibited higher expression of immune checkpoint molecules, lower tumor stemness index, and was much more sensitive to immunotherapy. Lastly, candidate drugs that aimed at LUAD subtype differentiation were identified. The derived IRGPs model is an adverse independent biomarker for estimating oncologic outcomes in LUAD patients, and may be helpful to formulate personalized immunotherapy strategy.     

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.     

Construction of an N6-methyladenosine lncRNA- and immune cell infiltration-related prognostic model in colorectal cancer

The present paper aims to shed light on the influence of N6-methyladenosine (m6A) long non-coding RNAs (lncRNAs) and immune cell infiltration on colorectal cancer (CRC). We downloaded workflow-type data and xml-format clinical data on CRC from The Cancer Genome Atlas project. The relationship between lncRNA and m6A was identified by using Perl and R software. Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed. Lasso regression was utilized to construct a prognostic model. Survival analysis was used to explore the relationship between clusters of m6A lncRNAs and clinical survival data. Differential analysis of the tumor microenvironment and an immune correlation analysis were used to determine immune cell infiltration levels in different clusters and their correlation with clinical prognosis. The expression of lncRNA was tightly associated with m6A. The univariate Cox regression analysis showed that lncRNA was a risk factor for the prognosis. Differential expression analysis demonstrated that m6A lncRNAs were partially highly expressed in tumor tissue. m6A lncRNA-related prognostic model could predict the prognosis of CRC independently. “ECM_RECEPTOR_INTERACTION” was the most significantly enriched gene set. PARP8 was overexpressed in tumor tissue and high-risk cluster. CD4 memory T cells, activated resting NK cells, and memory B cells were highly clustered in the high-risk cluster. All of the scores were higher in the low-risk group. m6A lncRNA is closely related to the occurrence and progression of CRC. The corresponding prognostic model can be utilized to evaluate the prognosis of CRC. m6A lncRNA and related immune cell infiltration in the tumor microenvironment can provide novel therapeutic targets for further research.