Mutational profiling of low-grade gliomas identifies prognosis and immunotherapy-related biomarkers and tumour immune microenvironment characteristics

Low-grade glioma (LGG) is a heterogeneous tumour with the median survival rate less than 10 years. Therefore, it is urgent to develop efficient immunotherapy strategies of LGG. In this study, we analysed mutation profiles based on the data of 510 LGG patients from the Cancer Genome Atlas (TCGA) database and investigated the prognostic value of mutated genes and evaluate their immune infiltration. Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to indicate the characteristics of gliomas that respond to immune checkpoint blockade (ICB) therapy. Univariate and multivariate cox regression analysis was performed to identify indicators to construct the nomogram model. 485 (95.47%) of 508 LGG samples showed gene mutation, and 9 mutated genes were significantly related to overall survival (OS), among which 6 mutated genes were significantly correlated with OS between mutation and wildtypes. Immune infiltration and immune score analyses revealed that these six mutated genes were significantly associated with tumour immune microenvironment in LGG. The response of LGG with different characteristics to ICB was evaluated by TIDE algorithm. Finally, CIC gene was screened through both univariate and multivariate Cox regression analyses, and the nomogram model was established to determine the potential prognostic value of CIC in LGG. Our study provides comprehensive analysis of mutated genes in LGG, supporting modulation of mutated genes in the management of 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.     

Prognostic and immunological role of Fam20C in pan-cancer

Background: The family with sequence similarity 20-member C (Fam20C) kinase plays important roles in physiopathological process and is responsible for majority of the secreted phosphoproteome, including substrates associated with tumor cell migration. However, it remains unclear whether Fam20C plays a role in cancers. Here, we aimed to analyze the expression and prognostic value of Fam20C in pan-cancer and to gain insights into the association between Fam20C and immune infiltration.Methods: We analyzed Fam20C expression patterns and the associations between Fam20C expression levels and prognosis in pan-cancer via the ONCOMINE, TIMER (Tumor Immune Estimation Resource), PrognoScan, GEPIA (Gene Expression Profiling Interactive Analysis), and Kaplan–Meier Plotter databases. After that, GEPIA and TIMER databases were applied to investigate the relations between Fam20C expression and immune infiltration across different cancer types, especially BLCA (bladder urothelial carcinoma), LGG (brain lower grade glioma), and STAD (stomach adenocarcinoma).Results: Compared with adjacent normal tissues, Fam20C was widely expressed across many cancers. In general, Fam20C showed a detrimental role in pan-cancer, it was positively associated with poor survival of BLCA, LGG, and STAD patients. Specifically, based on TCGA (The Cancer Genome Atlas) database, a high expression level of Fam20C was associated with worse prognostic value in stages T2–T4 and stages N0–N2 in the cohort of STAD patients. Moreover, Fam20C expression had positive associations with immune infiltration, including CD4⁺ T cells, macrophages, neutrophils, and dendritic cells, and other diverse immune cells in BLCA, LGG, and STAD.Conclusion: Fam20C may serve as a promising prognostic biomarker in pan-cancer and has positive associations with immune infiltrates.     

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients’ prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein–protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8⁺ T cells and neutrophils were significantly related to patients’ prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.     

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     

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.     

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 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.

     

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.     

Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer

Tumour microenvironment (TME) is crucial to tumorigenesis. This study aimed to uncover the differences in immune phenotypes of TME in endometrial cancer (EC) using Uterine Corpus Endometrial Carcinoma (UCEC) cohort and explore the prognostic significance. We employed GVSA enrichment analysis to cluster The Cancer Genome Atlas (TCGA) EC samples into immune signature cluster modelling, evaluated immune cell profiling in UCEC cohort (n = 538) and defined four immune subtypes of EC. Next, we analysed the correlation between immune subtypes and clinical data including patient prognosis. Furthermore, we analysed the expression of immunomodulators and DNA methylation modification. The profiles of immune infiltration in TCGA UCEC cohort showed significant difference among four immune subtypes of EC. Among each immune subtype, natural killer T cells (NKT), dendritic cells (DCs) and CD8⁺T cells were significantly associated with EC patients survival. Each immune subtype exhibited specific molecular classification, immune cell characterization and immunomodulators expression. Moreover, the expression immunomodulators were significantly related to DNA methylation level. In conclusion, the identification of immune subtypes in EC tissues could reveal unique immune microenvironments in EC and predict the prognosis of EC patients.     

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.     

Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients

Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear.Method: Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application.Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score.Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.     

The relationship among Girdin DNA methylation,its high expression,and immune infiltration in hepatocellular carcinoma: Clues from in silico analysis

Objective: The aim of the present study was to explore the relationship among Girdin DNA methylation, its high expression, and immune infiltration in human hepatocellular carcinoma (HCC).Materials and methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases were used to compare Girdin mRNA expression between HCC tissues and normal tissues, and determine the relationship between Girdin expression and HCC prognosis. TCGA database was also used to analyze the expression of Girdin and its methylation status, as well as the relationship between Girdin DNA methylation and HCC prognosis. The Tumor IMmune Estimation Resource (TIMER) database was used to explore the correlation between Girdin expression and HCC immune infiltration.Results: Girdin expression was elevated in HCC tissues compared with that in normal tissues. The degree of methylation at cg03188526, a CpG site in the Girdin gene body, was positively correlated with Girdin mRNA expression, while high Girdin expression and cg03188526 hypermethylation were both correlated with poor HCC prognosis. Additionally, HCC tissue with high Girdin expression exhibited abundant immune infiltration, and the high Girdin expression was associated with a worse prognosis in macrophage-enriched HCC specimens.Conclusion: Our findings indicated that Girdin likely functions as an oncogene in HCC and that hypermethylation at cg03188526 in the Girdin gene body may explain the high Girdin expression levels in HCC tissue. Furthermore, we report for the first time that the adverse effects of high Girdin expression in HCC patients may be partially mediated by tumor macrophage infiltration.     

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

TGFβ2 is a prognostic‐related biomarker and correlated with immune infiltrates in gastric cancer

TGFβ2 is an essential regulator of immune cell functionality, but the mechanisms whereby it drives immune infiltration in gastric cancer remain uncertain. The Oncomine and Tumor Immunoassay Resource (TIMER) databases were used for assessing the expression of TGFβ2, after which TIMER was used to explore the relationship between TGFβ2 and tumour immune infiltration. Finally, we assessed how TGFβ2 expression correlated with the expression of a set of marker genes associated with immune infiltration using TIMER and GEPIA. We determined TGFβ2 expression to be significantly correlated with outcome in multiple types of cancer in the Cancer Genome Atlas (TCGA), with the effect being particularly pronounced in gastric cancer. Furthermore, elevated TGFβ2 expression was found to be significantly correlated with gastric cancer N staging, and with the expression of a variety of immune markers associated with particular immune cell subsets. These results indicate that TGFΒ2 is associated with patient outcome and tumour immune cell infiltration in multiple cancer types. This suggests that TGFβ2 is a key factor which governs immune cell recruitment to gastric cancer tumours, potentially playing a vital role in governing immune cell infiltration and thus representing a valuable prognostic biomarker in gastric cancer patients.     

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.     

A six-gene-based signature for breast cancer radiotherapy sensitivity estimation

Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.     

Zinc Finger E-Box Binding Homeobox 2 as a Prognostic Biomarker in Various Cancers and Its Correlation with Infiltrating Immune Cells in Ovarian Cancer

This study investigated the expression of zinc finger E-box binding homeobox 2 (ZEB2), its prognostic significance in various cancers, and the correlation between ZEB2 and infiltrating immune cells and ZEB2-related proteins in ovarian cancer (OV). The Gene Expression Profiling Interactive Analysis tool was used to analyze RNA sequencing data and cancer survival rates, based on normal and tumor tissue data available in The Cancer Genome Atlas (TCGA) database. The Kaplan–Meier plotter and PrognoScan databases were used to analyze the prognostic value of ZEB2 in OV (n = 1144). The Tumor Immune Estimation Resource was used to investigate the correlation between ZEB2 and infiltrating immune cells in various cancers, including OV. High ZEB2 expression was associated with a poorer prognosis in OV. In OV, ZEB2 is positively correlated with CD8+T cells, neutrophils, macrophages, and dendritic cell invasion; and ZEB2 is negatively correlated with tumor-infiltrating B cells. The STRING database was used to investigate the correlations with ZEB2-related proteins. The results reveal that ZEB2 was positively correlated with SMAD1 and SMAD2 in OV. Our findings may serve as a potential prognostic biomarker, and provide novel insights into the tumor immunology in OV. Thus, ZEB2 may be a potential diagnostic and therapeutic target in OV.     

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.