Systematic pan-cancer analysis insights into ICAM1 as an immunological and prognostic biomarker.

Intercellular adhesion molecule 1 (ICAM1) is a cell surface adhesion glycoprotein in the immunoglobulin supergene family. It is associated with several epithelial tumorigenesis processes, as well as with inflammation. However, the function of ICAM1 in the prognosis of tumor immunity is still unclear. This study aimed to examine the immune function of ICAM1 in 33 tumor types and to investigate the prognostic value of tumors. Using datasets from the Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), Cancer Cell Lines Encyclopedia (CCLE), Human Protein Atlas (HPA), and cBioPortal, we investigated the role of ICAM1 in tumors. We explored the potential correlation between ICAM1 expression and tumor prognosis, gene mutations, microsatellite instability, and tumor immune cell levels in various cancers. We observed that ICAM1 is highly expressed in multiple malignant tumors. Furthermore, ICAM1 is negatively or positively associated with different malignant tumor prognoses. The expression levels of ICAM1 were correlated with the tumor mutation burden (TMB) in 11 tumors and with MSI in eight tumors. ICAM1 is a gene associated with immune infiltrating cells, such as M1 macrophages and CD8+ T cells in gastric and colon cancer. Meanwhile, the expression of ICAM1 is associated with several immune-related functions and immune-regulation-related signaling pathways, such as the chemokine signaling pathway. Our study shows that ICAM1 can be used as a prognostic biomarker in many cancer types because of its function in tumorigenesis and malignant tumor immunity.

Glioma risks associate with genetic polymorphisms of XRCC1 gene in Chinese population.

Glioma is the most common type of primary brain tumors in adults. Previous evidence indicates that the X-ray repair cross-complementing group 1 gene (XRCC1) is an important candidate gene which influencing the pathogenesis of glioma. This study aims to assess the potential associations between glioma risks and genetic polymorphisms of XRCC1 gene. A total of 1,286 Chinese Han ethnic subjects consisting of 638 glioma patients and 648 controls were recruited in this case-control study. The genotyping of XRCC1 genetic polymorphisms (c.482C>T, c.1161G>A, and c.1804C>A) were conducted using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), created restriction site-PCR (CRS-PCR) and DNA sequencing methods. Our data indicated that the allelic and genotypic frequencies of these genetic polymorphisms in glioma patients were significantly different from those of controls. We detected that the alleles/genotypes were statistically associated with the increased risks of glioma (for c.482C>T, TT versus (vs.) CC: OR = 2.24, 95% CI = 1.48-3.39, P < 0.001; T vs. C: OR = 1.30, 95% CI = 1.09-1.53, P = 0.003; for c.1161G>A, AA vs. GG: OR = 1.62, 95% CI = 1.11-2.35, P = 0.012; A vs. G: OR = 1.19, 95% CI = 1.01-1.41, P = 0.040; for c.1804C>A, AA vs. CC: OR = 2.12, 95% CI = 1.45-3.11, P < 0.001; A vs. C: OR = 1.32, 95% CI = 1.12-1.56, P = 0.001). Our findings suggest that these genetic polymorphisms of XRCC1 gene may influence glioma risks in Chinese Han ethnic subjects, and might be potential molecular markers for evaluating glioma risks.

Implications of inflammatory cell death-related IFNG and co-expressed RNAs (AC006369.1 and CCR7) in breast carcinoma prognosis, and anti-tumor immunity.

Objective: Interferon-γ (IFN-γ) encoded by IFNG gene is a pleiotropic molecule linked with inflammatory cell death mechanisms. This work aimed to determine and characterize IFNG and co-expressed genes, and to define their implications in breast carcinoma (BRCA). Methods: Transcriptome profiles of BRCA were retrospectively acquired from public datasets. Combination of differential expression analysis with WGCNA was conducted for selecting IFNG-co-expressed genes. A prognostic signature was generated through Cox regression approaches. The tumor microenvironment populations were inferred utilizing CIBERSORT. Epigenetic and epitranscriptomic mechanisms were also probed. Results: IFNG was overexpressed in BRCA, and connected with prolonged overall survival and recurrence-free survival. Two IFNG-co-expressed RNAs (AC006369.1, and CCR7) constituted a prognostic model that acted as an independent risk factor. The nomogram composed of the model, TNM, stage, and new event owned the satisfying efficacy in BRCA prognostication. IFNG, AC006369.1, and CCR7 were closely linked with the tumor microenvironment components (e.g., macrophages, CD4/CD8 T cells, NK cells), and immune checkpoints (notably PD1/PD-L1). Somatic mutation frequencies were 6%, and 3% for CCR7, and IFNG, and high amplification potentially resulted in their overexpression in BRCA. Hypomethylated cg05224770 and cg07388018 were connected with IFNG and CCR7 upregulation, respectively. Additionally, transcription factors, RNA-binding proteins, and non-coding RNAs possibly regulated IFNG and co-expressed genes at the transcriptional and post-transcriptional levels. Conclusion: Collectively, our work identifies IFNG and co-expressed genes as prognostic markers for BRCA, and as possible therapeutic targets for improving the efficacy of immunotherapy.

Seven Hub Genes Predict the Prognosis of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network.

Purpose: This study was aimed at identifying hub genes and ceRNA regulatory networks linked to prognosis in hepatocellular carcinoma (HCC) and to identify possible therapeutic targets. Methods: Differential expression analyses were performed to detect the differentially expressed genes (DEGs) in the four datasets (GSE76427, GSE6764, GSE62232, and TCGA). The intersected DEmRNAs were identified to explore biological significance by enrichment analysis. We built a competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA. The mRNAs of the ceRNA network were used to perform Cox and Kaplan-Meier analyses to obtain prognosis-related genes, followed by the selection of genes with an area under the curve >0.8 to generate the random survival forest model and obtain feature genes. Furthermore, the feature genes were subjected to least absolute shrinkage and selection operator (LASSO) and univariate Cox analyses were used to identify the hub genes. Finally, the infiltration status of immune cells in the HCC samples was determined. Results: A total of 1923 intersected DEmRNAs were identified in four datasets and involved in cell cycle and carbon metabolism. ceRNA network was created using 10 lncRNAs, 67 miRNAs, and 1,923 mRNAs. LASSO regression model was performed to identify seven hub genes, SOCS2, MYOM2, FTCD, ADAMTSL2, TMEM106C, LARS, and KPNA2. Among them, TMEM106C, LARS, and KPNA2 had a poor prognosis. KPNA2 was considered a key gene base on LASSO and Cox analyses and involved in the ceRNA network. T helper 2 cells and T helper cells showed a higher degree of infiltration in HCC. Conclusion: The findings revealed seven hub genes implicated in HCC prognosis and immune infiltration. A corresponding ceRNA network may help reveal their potential regulatory mechanism.