Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas

The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein–protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8⁺ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.     

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

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

Identification of a prognostic signature of epithelial ovarian cancer based on tumor immune microenvironment exploration

This study aims to develop an immune-related genes (IRGs) prognostic signature to stratify the epithelial ovarian cancer (EOC) patients. We identified 332 up- and 154 down-regulated EOC-specific IRGs. As a result, candidate IRGs were idendified to construct prognostic models respectivy for overall survial and progression-free survival. The risk score was validated as a risk factor for prognosis and was used to built a combined nomogram. According to the IRG-related prognostic model, EOC patients were divided into high- and low- risk group and were further explored their association with tumor immune microenvironment (TME). CIBERSORT algorithm showed higher macrophages M1 cell, T cells follicular helper cell and plasma cells infiltrating levels in the low-risk group. In addition, the low-risk group was found with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. These findings may shed light on the development of novel immune biomarkers and target therapy of EOC.     

Prognostic value of infiltrating immune cells in clear cell renal cell carcinoma (ccRCC)

In this study, we attempted to evaluate the prognostic value of infiltrating immune/stromal cells in clear cell renal cell carcinoma (ccRCC), by using the immune scores and stromal scores based on the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” algorithm to represent the levels of infiltrating immune cells and stromal cells. We found that the infiltrating immune cells were associated with poor prognosis of ccRCC. To assess the role of infiltrating immune cells in ccRCC cells, first, we performed differentially expressed genes analysis and functional analysis for validation. The results showed that the underlying mechanism by which infiltrating immune cells promoted cancer progression involved in regulating the nuclear division, angiogenesis, and immune response. Next, we investigated the relationship between infiltrating immune cells and mutations in ccRCC cells. We found that the infiltrating immune cells have certain effects on genetic mutations. In conclusion, infiltrating immune cells within the tumor microenvironment can be used to predict prognosis in ccRCC.     

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.     

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK–STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.     

Exploration of a novel prognostic risk signatures and immune checkpoint molecules in endometrial carcinoma microenvironment

In this study, we devoted to investigate immune-related genes and tumor microenvironment (TME) in EC based on The Cancer Genome Atlas (TCGA) database. In total 799 up-regulated and 139 down-regulated immune-related and differentially expressed genes in EC were investigated for functional annotations and prognosis. By a conjoint Cox regression analysis, we built two risk models for OS and DFS, as well as the consistent nomograms. Immune-related pathways were revealed mostly enriched in the low-risk group. By further analyzing TME based on the risk signatures, the higher immune cell infiltration and activation, lower tumor purity and higher tumor mutational burden were found in low-risk group, which presented a better prognosis. Both the expression and immunophenoscore of immune checkpoints PD-1, CTLA4, PD-L1 and PD-L2 increased significantly in low-risk group. These findings may provide new ideas for novel biomarkers and immunotherapy targets in EC.     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.     

Identification of key genes and novel immune infiltration-associated biomarkers of sepsis

Sepsis is the major cause of mortality in the intensive care unit. The aim of this study was to identify the key prognostic biomarkers of abnormal expression and immune infiltration in sepsis. In this study, a total of 36 differentially expressed genes were identified to be mainly involved in a number of immune-related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The hub genes (MMP9 and C3AR1) were significantly related to the prognosis of sepsis patients. The immune infiltration analysis indicated a significant difference in the relative cell content of naive B cells, follicular Th cells, activated NK cells, eosinophils, neutrophils and monocytes between sepsis and normal controls. Weighted gene co-expression network analysis and a de-convolution algorithm that quantifies the cellular composition of immune cells were used to analyse the sepsis expression data from the Gene Expression Omnibus database and to identify modules related to differential immune cells. CEBPB is the key immune-related gene that may be involved in sepsis. Gene set enrichment analysis revealed that CEBPB is involved in the processes of T cell selection, B cell–mediated immunity, NK cell activation and pathways of T cells, B cells and NK cells. Therefore, CEBPB may play a key role in the biological and immunological processes of sepsis.     

Gene expression and DNA methylation analyses suggest that immune process-related ADCY6 is a prognostic factor of luminal-like breast cancer

Breast cancer is a malignant tumor that seriously threatens women's health, and luminal-like cancer subtypes account for the majority of the cases. The purpose of this study was to investigate the relationships among DNA methylation, gene expression profile, and the tumor-immune microenvironment of luminal-like breast cancer, and to identify the potential key genes that regulate immune cell infiltration in luminal-like breast cancer. The ESTIMATE algorithm was applied to calculate immune scores and stromal scores of patients with breast cancer. Kaplan-Meier curves were generated for survival analysis. The clusterProfile package was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Correlations between ADCY6 expression and immune cell infiltration-related pathways were analyzed by gene set variation analysis. R software was used for the statistical analysis and figure generation. Disease-free survival was higher in the immune score-high group than it was in the immune score-low group, while the stromal score had no correlation with prognosis. There were 515 genes that differed in both gene expression and DNA methylation levels, and these genes were mainly enriched in immune process-related pathways. ADCY6 was enriched in module A of the PPI network. Patients with downregulation and hypermethylation of ADCY6 associated with a better prognosis. ADCY6 expression was negatively correlated with the activation of immune process-related signaling pathways, immune checkpoint receptors, and ligands, except for CLEC4G. DNA methylation was found to be involved in the regulation of the key cellular pathways of luminal-like breast cancer immune cell infiltration. Additionally, ADCY6 was identified as a prognostic factor involved in the DNA methylation-regulated immune processes in luminal-like breast cancer.     

Survival prediction and response to immune checkpoint inhibitors: A prognostic immune signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers all over the world. Several studies have explored if immune-related genes and tumor immune microenvironment could play roles in HCC prognoses. This study is aimed at developing a prognostic signature of HCC based on immune-related genes or tumor immune microenvironment to predict survival and response to immune checkpoint inhibitors (ICIs). We constructed a prognostic signature using bioinformatics method and validated its predictive capability. The mechanisms of the signature prediction were explored with The Cancer Immunome Atlas (TCIA) and mutation analysis. We also explored the association between the signature and immunophenoscore (IPS), which is the marker of ICIs response. A 6 immune-related-gene (6-IRG) signature was developed. It was revealed in a multivariate analysis that the 6-IRG signature was an independent prognostic factor of overall survival and progression-free interval among HCC patients. In the high-risk group of 6-IRG signature score, macrophage M0 cells and regulatory T cells, which are observed associated with poor overall survival in our study, were higher. The low-risk group had a higher IPS, which meant a better response to ICIs. Taken together, we constructed a reliable 6-IRG signature for prediction of survival and response to ICIs. The signature needs further testing for clinical application.     

Tumor-derived exosomal circPSMA1 facilitates the tumorigenesis,metastasis, and migration in triple-negative breast cancer (TNBC) through miR-637/Akt1/β-catenin (cyclin D1) axis

Circular RNAs (circRNAs) are increasingly gaining importance and attention due to their diverse potential functions and their value as diagnostic biomarkers (disease specific). This study aims to explore the novel mechanisms by which exosome-contained circRNAs promote tumor development and metastasis in TNBC. We identified increased circRNA circPSMA1 in TNBC cells, their exosomes, and serum exosomes samples from TNBC patients. The overexpression of circPSMA1 promoted TNBC cell proliferation, migration, and metastasis both in vitro and in vivo. Moreover, we investigated the tumor-infiltrating immune cells (TICs) or stromal components in immune microenvironment (IME), and identified the significant differences in the immune cells between TNBC and non-TNBC samples. Mechanistically, circPSMA1 acted as a “miRNAs sponge” to absorb miR-637; miR-637 inhibited TNBC cell migration and metastasis by directly targeted Akt1, which recognized as a key immune-related gene and affected downstream genes β-catenin and cyclin D1. Subsequent co-culture experiments also demonstrated that exosomes from TNBC carrying large amounts of circPSMA1 could transmit migration and proliferation capacity to recipient cells. Kaplan–Meier plots showed that high expression of Akt1 and low expression of mir-637 are highly correlated with poor prognosis in patients with lymph node metastasis of TNBC. Collectively, all these results reveal that circPSMA1 functions as a tumor promoter through the circPSMA1/miR-637/Akt1-β-catenin (cyclin D1) regulatory axis, which can facilitate the tumorigenesis, metastasis, and immunosuppression of TNBC. Our research proposes a fresh perspective on novel potential biomarkers and immune treatment strategies for TNBC.Subject terms: Breast cancer, Cancer microenvironment     

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.     

Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer

The immune system and the tumor interact closely during tumor development. Aberrantly expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. The present study aimed to build a risk scoring system to improve the prognosis of GC patients. In the present study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. About 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. About 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, ‘clusterProfiler’. The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.     

Mining TCGA database for screening and identification of hub genes in kidney renal clear cell carcinoma microenvironment

To evaluate the diagnosis and prognosis of the tumor microenvironment (immunization and stromal cells) in kidney renal clear cell carcinoma (KIRC), KIRC cases selected from The Cancer Genome Atlas database were divided into two groups according to the ESTIMATE algorithm-derived immune scores. Our data suggested that the Von Hippel-Lindau mutations and pathologic grades are associated with immune scores. Importat ntly, we identified 173 differential expression genes (DEGs) associated with prognosis in patients with KIRC. Consequently, Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on these DEGs, which included immune response, defense response, intrinsic to the plasma membrane, positive regulation of immune system process, and cytokine binding. Next, the protein-protein interaction network of DEGs and the most significant module was constructed. Five hub genes were identified and analyzed using biological analysis. The survival analysis of the hub genes showed that KIRC patients with high gene expression of C2, MXRA8, TNFSF13B, and X-linked inhibitor of apoptosis protein-associated factor 1 (XAF1) had worse overall survival, and MXRA8, TNFSF13B, and XAF1 alteration were significantly associated with disease-free survival (DFS). In addition, high gene expression of XAF1 alteration showed better DFS. Conclusion: we identified a list of microenvironment-related genes that are useful for understanding the molecular mechanisms and prognosis of KIRC.     

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。     

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。     

Prognostic and predictive significance of immune cells infiltrating cutaneous melanoma

The tumor microenvironment is shaped by interactions between malignant cells and host cells representing an integral component of solid tumors. Host cells, including elements of the innate and adaptive immune system, can exert both positive and negative effects on the outcome of the disease. In melanoma, studies on the prognostic impact of the lymphoid infiltrate in general, and that of T cells, yielded controversial results. According to our studies and data in the literature, a high peritumoral density of activated T cells, increased amount of B lymphocytes and mature dendritic cells (DCs) predicted longer survival, while intense infiltration by plasmacytoid DCs or neutrophil granulocytes could be associated with poor prognosis. Besides its prognostic value, evaluation of the components of immune infiltrate could provide biomarkers for predicting the efficacy of the treatment and disease outcome in patients treated with immunotherapy or other, non‐immune‐based modalities as chemo‐, radio‐, or targeted therapy.