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.

Single-cell mechanistic studies of radiation-mediated bystander effects.

Ionizing radiation (IR) has been widely used in the diagnosis and treatment of clinical diseases, with radiation therapy (RT) being particularly rapid, but it can induce "bystander effects" that lead to biological responses in non-target cells after their neighboring cells have been irradiated. To help clarify how radiotherapy induces these effects, To help clarify how radiotherapy induces these effects, we analyzed single-cell RNA sequencing data from irradiated intestinal tissues on day 1 (T1 state), day 3 (T3 state), day 7 (T7 state), and day 14 (T14 state) after irradiation, as well as from healthy intestinal tissues (T0 state), to reveal the cellular level, molecular level, and involvement of different time irradiated mouse intestinal tissues in biological signaling pathways. In addition, changes in immune cell subpopulations and myeloid cell subpopulations after different radiation times were further explored, and gene regulatory networks (GRNs) of these cell subpopulations were constructed. Cellular communication between radiation-specific immune cells was explored by cell-to-cell communication events. The results suggest that radiotherapy trigger changes in immune cell subsets, which then reprogram the immune ecosystem and mediate systemic bystander effects. These radiation-specific immune cells participate in a wide range of cell-to-cell communication events. In particular, radiation-specific CD8+T cells appear to be at the core of communication and appear to persist in the body after recovery from radiotherapy, with enrichment analysis showing that radiation-specific CD8+ T cells are associated with ferroptosis. Thus, radiation-specific CD8+ T cells may be involved in cellular ferroptosis-mediated adverse effects caused by RT.

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.

Identification of key genes involved in the metastasis of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal malignant tumor in adults. The aim of the present study was to identify the key genes involved in ccRCC metastasis. Expression profiling data for ccRCC patients with metastasis and without metastasis were obtained from The Cancer Genome Atlas database. The datasets were used to identify differentially expressed genes (DEGs) between the metastasis group and the non-metastasis group using the DESeq2 package. Function enrichment analyses of DEGs were performed. The protein-protein interaction (PPI) network was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape for further analysis of the identified hub genes. A total of 472 DEGs were identified, including 247 that were upregulated and 225 that were downregulated in the metastasis group. Gene Ontology enrichment analysis revealed that DEGs were mainly enriched in cell transmembrane movement and mitotic cell cycle process. Kyoto Encyclopedia of Genes Genomes pathway analysis revealed that the DEGs were mainly involved in the 'cell cycle' (hsa04110), 'collecting duct acid secretion' (hsa04966), 'complement and coagulation cascades' (hsa04610) and 'aldosterone-regulated sodium reabsorption' (hsa04960) pathways. Using the PPI network, 35 hub genes were identified, and the majority of them were upregulated in ccRCC tissue compared with normal kidney tissue. The expression levels of certain hub genes (CDKN3, TPX2, BUB1B, CDCA8, UBE2C, NDC80, RRM2, NCAPG, NCAPH, PTTG1, FAM64A, ANLN, KIF4A, CEP55, CENPF, KIF20A, ASPM and HJURP) were significantly associated with overall survival and recurrence-free survival in ccRCC. The present study has identified key genes associated with the metastasis of ccRCC.

Systematic Analysis of Gene Expression Alteration and Co-Expression Network of Eukaryotic Initiation Factor 4A-3 in Cancer.

Purpose: Eukaryotic initiation factor 4A-3 (EIF4A3) is an RNA-binding protein (RBP) that is a core component of the exon junction complex (EJC). It has been identified as an important player in post-transcriptional regulation processes. Recently, investigations have focused on EIF4A3 dysfunction in carcinogenesis. The present study aims to determine whether EIF4A3 can serve as a prognostic marker and potential regulatory mechanism in human cancers. Materials and methods: EIF4A3 expression in various cancers was assessed using Oncomine. The Correlation between EIF4A3 expression and patient survival was evaluated using PrognoScan. EIF4A3 mutations in various cancers were investigated using cBioPortal. EIF4A3 co-expression networks in various cancers were established using Coexpedia. Finally, we analyzed potential functional roles of EIF4A3 using Gene Ontology and pathway enrichment analyses by FunRich V3. Results: EIF4A3 was overexpressed in common malignancies at the transcription levels. High incidences of the breast, lung, and urinary cancers were closely related to the prognostic index for survival. The most prevalent mutation in EIF4A3 was E59K/Q. The tumor necrosis factor-α (TNF-α)/nuclear factor-κB (NF-κB) signaling pathway was affected by these mutations. Co-expression networks showed that EIF4A3 regulates apoptosis and cell cycle via several cancer-related signal pathways, and promotes tumor cell migration, invasion and drug resistance. Conclusion: Our results suggest the potential role for EIF4A3 to serve as a diagnostic marker or therapeutic target for certain types of cancers.

Long non-coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection.

AIM: The aim of this study was to develop a novel long non-coding RNA (lncRNA) expression signature to accurately predict early recurrence for patients with hepatocellular carcinoma (HCC) after curative resection. METHODS: Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple lncRNAs with differential expression between early recurrence (ER) and non-early recurrence (non-ER) groups of patients with HCC. Least absolute shrinkage and selection operator for logistic regression models were used to develop an lncRNA-based classifier for predicting ER in the training set. An independent test set was used to validate the predictive value of this classifier. Furthermore, a co-expression network based on these lncRNAs and its highly related genes was constructed and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of genes in the network were carried out. RESULTS: We identified 10 differentially expressed lncRNAs, including three that were upregulated and seven that were downregulated in the ER group. The lncRNA-based classifier was constructed based on seven lncRNAs (AL035661.1, PART1, AC011632.1, AC109588.1, AL365361.1, LINC00861, and LINC02084), and its accuracy was 0.83 in the training set, 0.87 in the test set, and 0.84 in the total set. Receiver operating characteristic curve analysis showed the area under the curve was 0.741 in the training set, 0.824 in the test set, and 0.765 in the total set. A functional enrichment analysis suggested that the genes highly related to four lncRNAs are involved in the immune system. CONCLUSION: The expression profile of seven lncRNAs can effectively predict ER after surgical resection for HCC.