Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods.

To identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME). Transcriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of stromal and Immune cells in malignant tumor tissues using expression data) algorithm to calculate immune scores, stromal scores and estimate scores. Heatmap and volcano plots were applied for differential analysis. Venn plots were used for intersect genes selection. We used protein-protein interaction (PPI) networks and functional analysis to explore underlying pathways. After performing stepwise regression method and multivariate Cox analysis, we finally screened hub genes associated with ACC TME. We calculated risk scores (RS) for ACC cases based on multivariate Cox results and evaluated the prognostic value of RS shown by receiver operating characteristic curve (ROC). We investigated the association between hub genes with immune infiltrates supported by algorithm from online TIMER database. Gene expression profiles and clinical data were downloaded from TCGA. Lower immune scores were observed in disease with distant metastasis (DM) and locoregional recurrence (LR) than other cases (P = .0204). Kaplan-Meier analysis revealed that lower immune scores were significantly associated with poor overall survival (OS) (P = .0495). We screened 1649 differentially expressed genes (DEGs) and 1521 DEGs based on immune scores and stromal scores, respectively. Venn plots helped us find 1122 intersect genes. After analysing by cytoHubba from Cytoscape software, 18 hub genes were found. We calculated RS and ROC showed significantly predictive accuracy (area under curve (AUC) = 0.887). ACC patients with higher RS had worse survival outcomes (P < .0001). Results from TIMER (tumor immune estimation resource) database revealed that HLA-DOA was significantly related with immune cells infiltration. We screened a list of TME-related genes which predict poor survival outcomes in ACC patients from TCGA database.

Li X ，Gao Y ，Xu Z ，Zhang Z ，Zheng Y ，Qi F ... -  《Cancer Medicine》

Screening TCGA database for prognostic genes in lower grade glioma microenvironment.

To identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system. We downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database. Gene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated. We identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Ni J ，Liu S ，Qi F ，Li X ，Yu S ，Feng J ，Zheng Y ... -  《-》

Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer.

The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC). Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database. Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717-0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates. In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

Wang Y ，Li W ，Jin X ，Jiang X ，Guo S ，Xu F ，Su X ，Wang G ，Zhao Z ，Gu X ... -  《BMC CANCER》

Screening the Cancer Genome Atlas Database for Genes of Prognostic Value in Acute Myeloid Leukemia.

Object: To identify genes of prognostic value which associated with tumor microenvironment (TME) in acute myeloid leukemia (AML). Methods and Materials: Level 3 AML patients gene transcriptome profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data were extracted from the Genomic Data Commons (GDC) tool. Then, limma package was utilized for normalization processing. ESTIMATE algorithm was used for calculating immune, stromal and ESTIMATE scores. We examined the distribution of these scores in Cancer and Acute Leukemia Group B (CALGB) cytogenetics risk category. Kaplan-Meier (K-M) curves were used to evaluate the relationship between immune scores, stromal scores, ESTIMATE scores and overall survival. We performed clustering analysis and screened differential expressed genes (DEGs) by using heatmaps, volcano plots and Venn plots. After pathway enrichment analysis and gene set enrichment analysis (GESA), protein-protein interaction (PPI) network was constructed and hub genes were screened. We explore the prognostic value of hub genes by calculating risk scores (RS) and processing survival analysis. Finally, we verified the expression level, association of overall survival and gene interactions of hub genes in the Vizome database. Results: We enrolled 173 AML samples from TCGA database in our study. Higher immune score was associated with higher risk rating in CALGB cytogenetics risk category (P = 0.0396) and worse overall survival outcomes (P = 0.0224). In Venn plots, 827 intersect genes were screened with differential analysis. Functional enrichment clustering analysis revealed a significant association between intersect genes and the immune response. After PPI network, 18 TME-related hub genes were identified. RS was calculated and the survival analysis results revealed that high RS was related with poor overall survival (P < 0.0001). Besides, the survival receiver operating characteristic curve (ROC) showed superior predictive accuracy (area under the curve = 0.725). Finally, the heatmap from Vizome database demonstrated that 18 hub genes showed high expression in patient samples. Conclusion: We identified 18 TME-related genes which significantly associated with overall survival in AML patients from TCGA database.

Ni J ，Wu Y ，Qi F ，Li X ，Yu S ，Liu S ，Feng J ，Zheng Y ... -  《Frontiers in Oncology》

Identification of immune-related biomarkers in adrenocortical carcinoma: Immune-related biomarkers for ACC.

Emerging evidence has suggested that the tumor microenvironment, including immune infiltration, plays a crucially important role in tumor progression. Nevertheless, limited studies have been conducted on this topic in adrenocortical carcinoma. The present study aimed to explore the immune-related biomarkers in adrenocortical carcinoma. CIBERSORT was used to estimate the abundances of 22 kinds of immune cells, and univariable Cox analysis was performed to find survival-related immune cells with both Overall Survival (OS) and Progression-Free Interval (PFI). DESeq2 was applied to find differentially expressed genes between adrenocortical carcinoma and normal control samples; subsequently, weighted correlation network analysis and protein-protein interaction (PPI) network analysis were conducted to identify immune-related hub genes. xCell, TISIDB, and MsigDB were searched to validate the immune associations of hub genes. Eventually, univariable Cox and Kaplan-Meier analysis were used to assess the prognostic implications of the hub gene with the GEO database. Consequently, we identified two hub immune-related genes (ERN1, CEP55), GSEA revealed that both were mainly involved in tumor progression and immune response. ROC analysis indicated that ERN1 can accurately predict the 1-, 3-, and 5-year PFI, and CEP55 had the best performance for the prediction of both OS and PFI compared with other traits. Univariable Cox and Kaplan-Meier analysis showed that both genes have a significant effect on prognosis. Furthermore, both hub genes were validated in GEO datasets. The hub genes can provide better insights into tumor microenvironment and serve as potential biomarkers for immunotherapy in adrenocortical carcinoma.

Peng Y ，Song Y ，Ding J ，Li N ，Zhang Z ，Wang H ... -  《-》